CN107305247B - Channel model formula correction method, device and equipment - Google Patents

Channel model formula correction method, device and equipment Download PDF

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CN107305247B
CN107305247B CN201610262337.9A CN201610262337A CN107305247B CN 107305247 B CN107305247 B CN 107305247B CN 201610262337 A CN201610262337 A CN 201610262337A CN 107305247 B CN107305247 B CN 107305247B
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channel model
result data
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model formula
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CN107305247A (en
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刘永俊
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Huawei Technologies Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
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Abstract

The invention discloses a channel model formula correction method, a device and equipment, and belongs to the technical field of positioning. The method comprises the following steps: collecting m groups of positioning result data, wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relation between signal propagation characteristics and distances, i represents the positioning times, and i is 1,2. And correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula. The invention can correct the channel model formula in time, thereby improving the positioning precision.

Description

Channel model formula correction method, device and equipment
Technical Field
The present invention relates to the field of positioning technologies, and in particular, to a method, an apparatus, and a device for correcting a channel model formula.
Background
Positioning is a common technology and is widely applied to various fields such as mobile phone positioning, automobile positioning and the like. There are many ways of positioning, and most commonly, the positioning is performed by using a channel model formula.
In particular, a channel model formula is determined for a location area, the channel model formula including measured nodes N within the location area1，0And node N2，0Distance, signal propagation characteristics, channel attenuation parameters and the like between the nodes, thereby ensuring that the channel model formula can express the incidence relation between the signal propagation characteristics and the distance, and carrying out positioning on the node N at the unknown position in the positioning area according to the channel model formulavWhen positioning is carried out, the node N is measuredvNode N corresponding to any known position in the positioning areauAfter the signal propagation characteristics are obtained, the node N is calculated according to the channel model formula and the signal propagation characteristicsuAnd node NvFrom node N, according to node NuPosition estimation node NvThe position of (a).
For example, the channel model formula may be:
Figure BDA0000973834700000011
the signal propagation characteristic is the signal strength, d0Representing a node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength therebetween, N is a channel attenuation parameter, and the variable d represents the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvThe signal strength in between. Then, P is calculated0、d0And N, the channel model formula is determined for node NvWhen positioning is carried out, the node N is connecteduAnd node NvSignal strength P betweenrSubstituting into the channel model formula to obtain the distance d, and determining the node N according to the distance dvThe position of (a).
However, the propagation environment of the wireless signal in the positioning area often changes, and once the propagation environment changes, the originally established channel model formula cannot accurately represent the association relationship between the signal propagation characteristics and the distance in the changed propagation environment, and if the original channel model formula is still used for positioning, the positioning accuracy is reduced.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method, an apparatus, and a device for correcting a channel model formula. The technical scheme is as follows:
in a first aspect, a method for modifying a channel model formula is provided, where the method includes:
collecting m groups of positioning result data, wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relation between signal propagation characteristics and distances, i represents the positioning times, and i is 1,2.
And correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving d sent by positioning serveriAnd Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iTransmitted diAnd Pi。
With reference to the first aspect, in a second possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node Nv，iTransmitted location information and PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in a third possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node Nv，iThe sent position information and the P sent by the positioning server are receivediThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in a fifth possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node N sent by positioning serverv，iPosition information and P ofiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in a sixth possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node N sent by positioning serverv，iAnd receive node Nv，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in a seventh possible implementation manner of the first aspect, the collecting m sets of positioning result data includes:
receiving node N sent by positioning serverv，iAnd receive node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the first aspect, in an eighth possible implementation manner of the first aspect, the channel model is formulated as
Figure BDA0000973834700000031
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength therebetween, n represents a channel attenuation parameter,
Figure BDA0000973834700000041
denotes the mean square error of X and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the step of correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula includes:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000042
And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000043
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000044
according to P0', n ', and sigma ' to obtain a modified channel model formula
Figure BDA0000973834700000045
Figure BDA0000973834700000046
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、n、σ、P0', n ' and sigma ' are smoothed to obtain P0", n", and σ ", according to P0The channel model formula after the correction is obtained by the ' n ' and the ' sigma
Figure BDA0000973834700000047
Figure BDA0000973834700000051
With reference to the first aspect, in a ninth possible implementation manner of the first aspect, the channel model is formulated as
Figure BDA0000973834700000052
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength, alpha represents a channel attenuation parameter,
Figure BDA0000973834700000053
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the step of correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula includes:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000054
And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000055
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000056
according to P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure BDA0000973834700000057
Figure BDA0000973834700000061
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure BDA0000973834700000062
Figure BDA0000973834700000063
With reference to the first aspect, in a tenth possible implementation manner of the first aspect, the channel model is formulated as
Figure BDA0000973834700000064
Said signal propagation characteristic PiAs signal propagation time Pti；
Wherein a represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd nodeNvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure BDA0000973834700000065
σ represents the mean square error of X;
the step of correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula includes:
calculating a corrected first weighting parameter α' according to the distance and the signal propagation time in the m sets of positioning result data by using the following formula:
Figure BDA0000973834700000066
and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000067
and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000071
obtaining a corrected channel model formula according to a ', b' and sigma
Figure BDA0000973834700000072
Figure BDA0000973834700000073
Alternatively, the first and second electrodes may be,
smoothing the a, b, sigma, alpha ', b ' and sigma ' by adopting a filtering smoothing algorithm to obtain alpha ', b ' andσ ", obtaining the corrected channel model formula according to α", b "and σ
Figure BDA0000973834700000074
Figure BDA0000973834700000075
With reference to any one of the possible implementation manners of the first aspect, in an eleventh possible implementation manner of the first aspect, the filter smoothing algorithm is S ═ x × S + (1-x) × S';
wherein S represents an original parameter, S ' represents a correction parameter obtained by correcting S, S ' represents a parameter obtained by smoothing S and S ', x represents a weighting coefficient, and x is a number in a range of [0, 1);
alternatively, the filter smoothing algorithm is
Figure BDA0000973834700000076
Wherein j represents the number of correction times, S represents the original parameter, S (j-i + 1)' represents the correction parameter obtained after correcting S at the j-i +1 th time, S (j) represents the parameter obtained after smoothing processing at the j-th correction time, x represents the weighting coefficient, x represents the weight coefficient, and x represents the weight coefficientiIs a number in the range of [0, 1), and
Figure BDA0000973834700000077
in a second aspect, an apparatus for modifying a channel model formula is provided, the apparatus comprising:
a collecting module for collecting m groups of positioning result data, wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relation between signal propagation characteristics and distances, i represents the positioning times, and i is 1,2.
And the correction module is used for correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula.
With reference to the second aspect, in a first possible implementation manner of the second aspect, the collecting module is specifically configured to receive d sent by the positioning serveriAnd Pi(ii) a Or, the receiving node Nv，iTransmitted diAnd Pi。
With reference to the second aspect, in a second possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node Nv，iTransmitted location information and PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in a third possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node Nv，iThe sent position information and the P sent by the positioning server are receivediThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in a fourth possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in a fifth possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node N sent by positioning serverv，iPosition information and P ofiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in a sixth possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node N sent by positioning serverv，iAnd receive node Nv，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in a seventh possible implementation manner of the second aspect, the collection module is specifically configured to:
receiving node N sent by positioning serverv，iAnd receive node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
With reference to the second aspect, in an eighth possible implementation manner of the second aspect, the channel model is formulated as
Figure BDA0000973834700000091
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0Reference letter in betweenThe sign strength, n represents a channel fading parameter,
Figure BDA0000973834700000092
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the correction module is used for:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000093
And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000094
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000101
according to P0', n ', and sigma ' to obtain a modified channel model formula
Figure BDA0000973834700000102
Figure BDA0000973834700000103
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、n、σ、P0', n ' and sigma ' are smoothed to obtain P0", n", and σ ", according to P0”、n”The sum sigma' is obtained as a corrected channel model formula
Figure BDA0000973834700000104
Figure BDA0000973834700000105
With reference to the second aspect, in a ninth possible implementation manner of the second aspect, the channel model is formulated as
Figure BDA0000973834700000106
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength, alpha represents a channel attenuation parameter,
Figure BDA0000973834700000107
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the correction module is used for:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000108
And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000111
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000112
according to P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure BDA0000973834700000113
Figure BDA0000973834700000114
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure BDA0000973834700000115
Figure BDA0000973834700000116
With reference to the second aspect, in a tenth possible implementation manner of the second aspect, the channel model is formulated as
Figure BDA0000973834700000117
Said signal propagation characteristic PiAs signal propagation time Pti；
Where α represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd node NvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure BDA0000973834700000118
σ represents the mean square error of X;
the correction module is used for:
calculating a corrected first weighting parameter α' according to the distance and the signal propagation time in the m sets of positioning result data by using the following formula:
Figure BDA0000973834700000119
and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000121
and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000122
obtaining a corrected channel model formula according to a ', b' and sigma
Figure BDA0000973834700000123
Figure BDA0000973834700000124
Alternatively, the first and second electrodes may be,
smoothing the a, b, sigma, a ', b' and sigma 'by adopting a filtering smoothing algorithm to obtain a', b 'and sigma', and obtaining a corrected channel model formula according to the a ', b' and sigma
Figure BDA0000973834700000125
Figure BDA0000973834700000126
In an eleventh possible implementation manner of the second aspect, in combination with any one of the possible implementation manners of the second aspect, the filter smoothing algorithm is S ═ x × S + (1-x) × S';
wherein S represents an original parameter, S ' represents a correction parameter obtained by correcting S, S ' represents a parameter obtained by smoothing S and S ', x represents a weighting coefficient, and x is a number in a range of [0, 1);
alternatively, the filter smoothing algorithm is
Figure BDA0000973834700000127
Wherein j represents the number of correction times, S represents the original parameter, S (j-i + 1)' represents the correction parameter obtained after correcting S at the j-i +1 th time, S (j) represents the parameter obtained after smoothing processing at the j-th correction time, x represents the weighting coefficient, x represents the weight coefficient, and x represents the weight coefficientiIs a number in the range of [0, 1), and
Figure BDA0000973834700000128
in a third aspect, there is provided a channel model formula correction device, including: the receiver, the transmitter, the memory and the processor are respectively connected with the processor, the memory stores program codes, and the processor is used for calling the program codes and executing the following operations:
collecting m groups of positioning result data, wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relation between signal propagation characteristics and distances, i represents the positioning times, and i is 1,2.
And correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula.
With reference to the third aspect, in a first possible implementation manner of the third aspect, the channel model formula modifying apparatus is a user equipment or a server.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the method, the device and the equipment provided by the embodiment of the invention, the channel model formula is corrected according to the multiple groups of positioning result data and the channel model formula by collecting the multiple groups of positioning result data, so that the corrected channel model formula is obtained, the channel model formula can be corrected in time when the propagation environment of the wireless signal changes, and the positioning precision is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a positioning system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a positioning system according to an embodiment of the present invention;
fig. 3 is a flowchart of a channel model formula modification method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of the operation provided by the embodiment of the invention;
FIG. 5 is a schematic flow chart of the operation provided by the embodiment of the invention;
FIG. 6 is a schematic flow chart of the operation provided by the embodiment of the invention;
FIG. 7 is a schematic flow chart of operations provided by an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a channel model formula correction device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic structural diagram of a positioning system according to an embodiment of the present invention. Referring to fig. 1, the positioning system includes: the position of the anchor node is known, the position of the blind node is unknown, and the blind node can send a wireless signal to the anchor node or receive the wireless signal sent by the anchor node. And the at least one blind node is connected with the correction server through a network.
The channel model formula is used for representing the incidence relation between the signal propagation characteristics and the distance, and after the signal propagation characteristics are known, the corresponding distance can be calculated according to the channel model formula.
With node NuRepresenting anchor nodes by node NvRepresenting blind nodes, u and v representing node numbers, when node N is to be addressedvWhen positioning is carried out, the node N can be obtainedvAnd node NuSignal propagation characteristic P betweenrThen according to the channel model formula obtained in advance and the signal propagation characteristic PrCalculate the node NuAnd the node NvD, according to the node NuAnd the calculated distance d, the node N is estimatedvThe position of (a).
Further, the signal propagation characteristic may be signal strength or signal propagation time, node NuAnd the node NvSignal strength P betweenrRefers to node NuAnd the node NvThe signal strength of the wireless signal propagating therebetween. Node NuAnd the node NvSignal propagation time P in betweentRefers to the wireless signal slave node NuAnd the node NvTime of transmission to reception.
In the embodiment of the invention, in order to avoid reducing the positioning accuracy when the wireless propagation environment changes, a correction server is arranged for correcting the channel model formula.
The correction server is used for collecting m groups of positioning result data, and correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula. Wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iAnd i represents the positioning times, i is 1,2.. m, and u and v represent node numbers.
In practical application, any node Nv，iCan be based on a channel model formula and a node Nu，iThe obtained d can be positioned when the positioning is finishediAnd PiUploading to a correction server, receiving a node N by the correction serverv，iTransmitted diAnd Pi. After m times of positioning, the correction server can collect m groups of positioning result data, so as to correct the channel model formula. Or, node Nv，iThe estimated position information and P may also be combinediUploading to a correction server, wherein the position information is used for indicating the node Nv，iBy the correction server, receiving node Nv，iTransmitted location information and PiAccording to node Nu，iAnd node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni. After m times of positioning, the correction server can collect m groups of positioning result data, so as to correct the channel model formula.
In addition, the above embodiment is only the node N in the positioning processv，iAs a receiver of the wireless signal, and node Nu，iAs an example of a sender of wireless signals, i.e. by node Nv，iTo measure PiAnd in another embodiment, node Nv，iCan be used as a transmitter of wireless signals, and the node Nu，iAs a radioReceiver of signal, node Nu，iMeasurement of PiThereafter, P can be sent to the location serveriSending P from the location server to the correction serveri. Or, node Nu，iMeasurement of PiThereafter, P can be sent to the correction serveriThe correction server can receive the node Nu，iTransmitted Pi。
The positioning system shown in fig. 1 adopts a distributed positioning mode, and each blind node calculates its position according to the measurement information. In another possible implementation manner, a centralized positioning manner may also be adopted, and the positioning server unifies the positioning of the blind nodes, which is described in detail in the following embodiment.
Fig. 2 is a schematic structural diagram of a positioning system according to an embodiment of the present invention. Referring to fig. 2, the positioning system includes: the blind node can send wireless signals to the anchor node or receive wireless signals sent by the anchor node, the blind node is connected with the correction server through a network, the positioning server is connected with the correction server through the network, or the positioning server and the correction server can be located in the same server, namely the positioning server and the correction server are different functional modules on the same server. The anchor node may be connected to the correction server through a network or may not be connected to the correction server, and the anchor node may be connected to the positioning server through a network or may not be connected to the positioning server.
With node NuRepresenting anchor nodes by node NvRepresenting blind nodes, when node N is to be addressedvWhen positioning is performed, node NvNode N capable of uploading measured data to positioning servervAnd node NuSignal propagation characteristic P betweenrThe positioning server obtains the signal propagation characteristic PrThen according to the channel model formula obtained in advance and the signal propagation characteristic PrCalculate the node NvAnd node NuAccording to the distance between the nodes NuAnd the calculated distance d, the node N is estimatedvPosition ofAnd (4) placing.
In the embodiment of the invention, in order to avoid reducing the positioning accuracy when the wireless propagation environment changes, a correction server is arranged for correcting the channel model formula.
In practical application, any node N is calculated every time the positioning server calculatesv，iPositioning result data d ofiAnd PiIn time, the obtained d can beiAnd PiUploading to a correction server, and receiving d sent by the positioning server by the correction serveriAnd Pi. After m times of positioning, the correction server can collect m groups of positioning result data, so as to correct the channel model formula.
It should be noted that, in the same positioning system, the distributed positioning and the centralized positioning may exist in a mixed manner. The correction server can collect the estimated position or distance of the blind node in distributed positioning and the corresponding signal propagation characteristics, and can also collect the estimated position or distance obtained by centralized positioning by the positioning server and the corresponding signal propagation characteristics. Moreover, if the correction server supports position estimation, the signal propagation characteristics obtained by blind node measurement can be collected, the position of the blind node is estimated, and the distance between the blind node and the anchor node is calculated, so that positioning result data is obtained.
Fig. 3 is a flowchart of a method for modifying a channel model formula according to an embodiment of the present invention. The execution subject of the embodiment of the present invention is a correction server, and referring to fig. 3, the method includes:
301. and collecting m groups of positioning result data.
In the embodiment of the invention, a channel model formula can be determined for the positioning area, the channel model formula is used for expressing the incidence relation between the signal propagation characteristics and the distance, and the node N at any unknown position can be subjected to the channel model formulav，iAnd (6) positioning. Correspondingly, the positioning result data comprises the nodes N in the positioning areav，iAnd node Nu，iA distance d betweeniAnd signal propagation characteristic Pi. Wherein, node Nu，iCan be any already positioned part in the positioning regionA node with known position, for which node N is the present embodimentu，iNor are they intended to be limiting. Where u and v denote node numbers, i denotes the number of times of positioning, i is 1,2u，iAnd node Nv，iThe embodiments of the present invention are not limited to the above embodiments, and may be the same or different.
In addition, the signal propagation characteristic PiMay be the signal strength PriOr signal propagation time PtiNode Nv，iAnd node Nu，iSignal strength P betweenriRefers to the signal strength of a wireless signal propagating between a blind node and an anchor node. Node Nv，iAnd node Nu，iSignal propagation time P in betweentiRefers to the wireless time slave node Nv，iAnd node Nu，iTime of transmission to reception. Wherein, at node Nv，iIn the process of positioning, the node Nv，iCan be used as a transmitting node, node Nu，iAs a receiving node, node Nv，iTo node Nu，iTransmitting radio signals, or, node Nv，iCan be used as a receiving node, node Nu，iAs transmitting node, node Nu，iTo node Nv，iAnd transmitting the wireless signal. The embodiment of the present invention is not limited thereto.
Node Nv，iTo node Nu，iWhen transmitting a radio signal, the signal strength PriIs node Nu，iMeasured node Nv，iSignal strength of transmitted signal, the signal propagation time PtiIs a wireless signal slave node Nv，iTo node Nu，iThe time of propagation; node Nu，iTo node Nv，iWhen transmitting a radio signal, the signal strength PriIs node Nv，iMeasured node Nu，iSignal strength of transmitted signal, the signal propagation time PtiIs a wireless signal slave node Nu，iTo node Nv，iThe time of propagation.
It should be noted that the embodiment of the present invention is based on the node N onlyu，iHejie (Chinese character)Point Nv，iWithout limiting the wireless signal to be processed by node Nu，iIs sent to node Nv，iOr by node Nv，iIs sent to node Nu，i。
In the embodiment of the invention, the propagation environment of the wireless signal changes due to various factors such as the movement of articles, the movement of people and the like in the positioning area, and when the propagation environment of the wireless signal changes, the channel model formula also changes, otherwise, the original channel model formula cannot accurately represent the incidence relation between the signal propagation characteristics and the distance in the changed propagation environment, and if the original channel model formula is still adopted for positioning, the positioning precision is reduced.
Considering that the propagation environment in the positioning region is usually gradually changed when the propagation environment changes, the change caused by the environmental change is directly reflected in the positioning result data, that is, the positioning result data can reflect the association relationship between the signal propagation characteristics and the distance in the current propagation environment, so that in the process of positioning according to the channel model formula, a plurality of groups of positioning result data are collected, and the channel model formula is corrected according to the plurality of groups of positioning result data.
For the ith positioning, the positioning result data comprises a node Nv，iAnd node Nu，iA distance d betweeniAnd signal propagation characteristic PiCollection node Nv，iThe process of locating the result data may include the following cases:
in the first case: node Nv，iPositioning result data d obtained after positioning is finishediAnd PiSent to the correction server, and the node N is received by the correction serverv，iTransmitted diAnd PiAnd store diAnd Pi。
In the second case: when the positioning server carries out centralized positioning, the positioning server carries out positioningiAnd PiD is sent to the correction server, and the correction server receives d sent by the positioning serveriAnd PiAnd store the distanceFrom diAnd signal propagation characteristic Pi。
In the third case: node Nv，iSending node N to correction server after positioning is completedv，iPosition information and P ofiThe position information is used for indicating the node Nv，iThe position of (a). The correction server receiving node Nv，iTransmitted location information and PiAnd according to node Nv，iPosition and node Nu，iPosition of, compute node Nv，iAnd node Nu，iTo obtain d fromiAnd Pi。
In a fourth case: when the positioning is carried out by the positioning server in a centralized way, the node Nv，iSending node N to correction server after positioning is completedv，iThe location information of (1). The positioning server sends P to the correction serveriThe correction server receiving node Nv，iThe sent position information and the P sent by the positioning server are receivediAnd according to node Nv，iPosition and node Nu，iPosition of, compute node Nv，iAnd node Nu，iTo obtain d fromiAnd Pi。
In the fifth case: node Nv，iSending node N to correction server after positioning is completedv，iPosition information of, node Nu，iSending P measured at positioning to correction serveriThe correction server receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted PiAnd according to node Nv，iPosition and node Nu，iPosition of, compute node Nv，iAnd node Nu，iA distance d betweeniThereby obtaining diAnd Pi。
In the sixth case: when the positioning server carries out centralized positioning, the positioning server obtains a node N in positioningv，iPosition information and P ofiSending node N to the correction serverv，iPosition information and P ofi. Node N for receiving and sending by positioning server through correction serverv，iPosition information and P ofiAccording to node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeniThereby obtaining diAnd Pi。
In the seventh case: when the positioning server carries out centralized positioning, the positioning server obtains a node N in positioningv，iAfter the position information is sent to a correction server, node Nv，iSending P to a correction serveriIf the correction server receives the node N sent by the positioning serverv，iAnd receive node Nv，iTransmitted PiAccording to node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeniThereby obtaining diAnd Pi。
In the eighth case: when the positioning server carries out centralized positioning, the positioning server obtains a node N in positioningv，iAfter the position information is sent to a correction server, node Nu，iSending P to a correction serveriIf the correction server receives the node N sent by the positioning serverv，iAnd receive node Nu，iTransmitted PiAccording to node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeniThereby obtaining diAnd Pi。
It should be noted that the embodiments of the present invention are described only by taking the above collection methods as examples, but the method for collecting the positioning result data by the correction server is not limited.
In practical application, the correction server estimates the node N according to the positioning timev，iLocation and known node Nu，iPosition calculation node N ofv，iAnd node Nu，iA distance d betweeniCalculated distance diAnd node Nv，iAnd node Nu，iActual distance r betweeniThe relationship of (1) is:
Figure BDA0000973834700000191
riis an actual distance, A is a constant, ZiIs a Gaussian random variable with zero mean and mean square error of sigma.
Further, the greater the number m of positioning result data, the higher the accuracy of the correction result, and therefore, after step 301 and before step 302, the method may further include: the correction server firstly obtains the number of the positioning result data, judges whether the number reaches a preset threshold value, if the number reaches the preset threshold value, namely m is equal to the preset threshold value, correction is carried out, if the number does not reach the preset threshold value, the positioning result data are continuously collected, and after the number of the collected positioning result data reaches the preset threshold value, the preset threshold value set positioning result data are collected, correction is carried out again, so that the accuracy rate of the correction result is ensured. The preset threshold may be set by a technician during development, or may be set by the modification server as a default, which is not limited in the embodiment of the present invention.
302. And correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula.
In the embodiment of the present invention, the channel model formula includes at least one parameter, a distance variable, and a signal propagation characteristic variable, where the at least one parameter may be a reference signal strength, a reference signal propagation time, and the like, and when the channel model formula is modified, at least one parameter in the channel model formula needs to be modified. In the correction process, the correction server may use m sets of positioning result data as known quantities, derive a formula for calculating parameters according to the channel model formula by using a minimum mean square error criterion or other statistical methods, and calculate at least one new parameter according to the m sets of positioning result data and the derived formula.
Alternatively, the channel model formula may be any one of the following three types:
the first method comprises the following steps: the channel model is formulated as
Figure BDA0000973834700000201
Signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength therebetween, n represents a channel attenuation parameter,
Figure BDA0000973834700000202
σ represents the mean square error of X; the variable d represents the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
node N1，0And node N2，0The node used for measurement in the positioning area can be selected by a measuring person or automatically selected from nodes with known positions. To node N1，0And node N2，0The distance between the nodes and the wireless signals propagated between the nodes are measured, and the node N can be obtained1，0And node N2，0Reference distance d between0And reference signal strength P0And obtaining the channel model formula.
And the second method comprises the following steps: the channel model is formulated as
Figure BDA0000973834700000211
Signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength, alpha represents a channel attenuation parameter,
Figure BDA0000973834700000212
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
and the third is that: the channel model is formulated as
Figure BDA0000973834700000213
Signal propagation characteristic PiAs signal propagation time Pti；
Where α represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd node NvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure BDA0000973834700000214
σ represents the mean square error of X;
note that the node N measured in obtaining the channel model formula1，0And node N2，0Node N in positioninguAnd node NvAre located in the same positioning area. When in actual application, the node N can be used1，0And node N2，0As node NuI.e. anchor node, based on node NuFor any node N with unknown positionvThe positioning is performed to improve the accuracy of the channel model formula. Alternatively, the node N may be removed from the location area1，0And node N2，0One node is selected from the other nodes as a node NuBased on node NuFor any node N with unknown positionvThe positioning is performed, which is not limited in the embodiment of the present invention.
Accordingly, the manner of correction is different for different channel model formulas. The step 302 may include any one of the following 3021-3023:
3021. for the first channel model formula above:
1. calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000221
2. And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000222
3. and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000223
after performing steps 1-3 above, the correction server may be based on P0', n ', and sigma ' to obtain a modified channel model formula
Figure BDA0000973834700000224
Figure BDA0000973834700000225
It should be noted that, since the sum of a large number of zero-mean random variables tends to zero, it is easy to prove that when m is large,
Figure BDA0000973834700000226
Figure BDA0000973834700000227
therefore, the temperature of the molten metal is controlled,
Figure BDA0000973834700000228
that is, in the positioning process, even if the node N is utilizedvDetermining node N from the estimated positionuAnd node NvThe estimated distance between the two as positioning result data can obtain accurate parameters as long as the data volume is large enough, and further the accurate parameters can be obtainedAn accurate channel model formula is obtained.
3022. For the second channel model formula above:
1. calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000231
2. And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000232
3. and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000233
after performing steps 1-3 above, the correction server may be based on P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure BDA0000973834700000234
Figure BDA0000973834700000235
3023. For the third channel model formula above:
1. calculating a corrected first weighting parameter a' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000236
2. and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000237
3. and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000241
after the above steps 1-3 are performed, the correction server can obtain the corrected channel model formula according to α ', b' and σ
Figure BDA0000973834700000242
Figure BDA0000973834700000243
The three channel models described above are only examples that include a gaussian random variable X, and in fact for a certain channel model:
Figure BDA0000973834700000244
Figure BDA0000973834700000245
Figure BDA0000973834700000246
for example, the correction may be performed by the above-described method.
It should be noted that there may be a plurality of channel model formulas, and the modified channel model formula is not limited in the embodiment of the present invention. Each channel model formula may include a plurality of parameters, and the correction server may correct any one or more of the plurality of parameters, and the corrected parameters are not limited in the embodiment of the present invention.
Further, a preset correction algorithm may be adopted during correction, and the preset correction algorithm may be a maximum likelihood algorithm or a polygon algorithm, which is not limited in the embodiment of the present invention.
It should be noted that, in the above steps 3021-3023, the modified channel model formula is obtained according to the modified parameters after the parameters in the channel model formula are modified. In another embodiment, since an error may occur when the correction is performed only according to the distances and the signal strengths in the m sets of positioning result data, in order to improve the correction accuracy, after the corrected parameters are calculated according to the distances and the signal strengths in the m sets of positioning result data, the original parameters and the corrected parameters may also be smoothed, and the corrected channel model formula may be obtained according to the smoothed parameters.
For example, for the above three channel model formulas, this step 302 may further include:
first, calculate to get P0', n ' and sigma ', and then adopting a filter smoothing algorithm to P0、n、σ、P0', n ' and sigma ' are smoothed to obtain P0", n", and σ ", according to P0The channel model formula after the correction is obtained by the ' n ' and the ' sigma
Figure BDA0000973834700000247
Figure BDA0000973834700000248
Second, P is obtained by calculation0', alpha ' and sigma ', and then adopting a filter smoothing algorithm to P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure BDA0000973834700000251
Figure BDA0000973834700000252
Thirdly, calculating to obtain a ', b ' and sigma ', smoothing the a, b, sigma, a ', b ' and sigma ' by adopting a filtering smoothing algorithm to obtain a ', b ' and sigma ', and obtaining a modified channel model formula according to the a ', b ' and sigma
Figure BDA0000973834700000253
Figure BDA0000973834700000254
Further, in one possible implementation, the filter smoothing algorithm may be S ═ x × S + (1-x) × S':
wherein S denotes an original parameter, S 'denotes a correction parameter obtained by correcting S, S ″ denotes a parameter obtained by smoothing S and S', x denotes a weighting coefficient, and x is a number in the range of [0, 1), and optionally, x is 0.5.
In a second possible implementation manner, the filter smoothing algorithm may also be
Figure BDA0000973834700000255
Wherein j represents the number of correction times, S represents the original parameter, S (j-i + 1)' represents the correction parameter obtained after correcting S at the j-i +1 th time, S (j) represents the parameter obtained after smoothing processing at the j-th correction time, x represents the weighting coefficient, x represents the weight coefficient, and x represents the weight coefficientiIs a number in the range of [0, 1), and
Figure BDA0000973834700000256
it should be noted that, for any parameter of any channel model formula, the filtering smoothing algorithm described above may be adopted to perform smoothing processing, which is not limited in the embodiment of the present invention. Moreover, the two filter smoothing algorithms can be mixed for the same positioning system. For example, when the positioning system is just started, the initial parameters are usually determined by offline measurement, and at this time, a more complex filtering and smoothing algorithm, i.e., the second filtering and smoothing algorithm, may be used for correction, and after a period of correction, a more simple filtering and smoothing algorithm, i.e., the first filtering and smoothing algorithm, may be used.
The second point to be noted is that a plurality of anchor nodes N can be set in one positioning regionu. If the distance is to be estimated from the position determined by the positioning, two types of deviations occur when the propagation environment changes: unbiased deviation and biased deviation.
The unbiased deviation may affect the estimation result, but the statistical mathematical expectation is not changed, for example, the mean square error of the signal strength is large, but the mean value is unchanged (for example, when the flow rate is large, the signal fluctuation is large, and when the flow rate is small, the signal fluctuation is small), so the deviation does not affect the corrected channel model parameter, and the calculated parameter has a reference value.
The biased deviation affects not only the estimated result but also the mathematical expectation of statistics, such as the anchor node NuReduced transmit power or reduced signal quality due to changes in the surrounding environment, which may lead to a deviation of the positioning result from a particular anchor node Nu. The channel model parameters calculated from such deviations are inaccurate. However, since the propagation environment is gradual, i.e. only partially changed, the computed location statistics have a certain accuracy through the unchanged portion, i.e. the rest of the anchor nodes not affected by the change, and the computed parameters used for the computation of the parameters in the channel model formula are more accurate than the original values. By repeated correction, the true accurate value can be continuously approached.
A third point to be described is that the embodiment of the present invention is only described by taking one channel model formula as an example, and in practical applications, different channel model formulas can be obtained for different positioning areas, and accordingly, any channel model formula can be corrected by using the correction method provided by the embodiment of the present invention, which is not limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the channel model formula is corrected according to the multiple groups of positioning result data and the channel model formula by collecting the multiple groups of positioning result data, so that the corrected channel model formula is obtained, the channel model formula can be corrected in time when the propagation environment of the wireless signal changes, and the positioning precision is improved. Furthermore, smoothing is performed by adopting a filtering smoothing algorithm, so that the accuracy of the correction result is improved.
All the above-mentioned optional technical solutions can be combined arbitrarily to form the optional embodiments of the present invention, and are not described herein again.
The above embodiment is described by taking the correction server as an execution subject, and actually, the process of correcting the channel model formula may also be executed by the blind node and multiple servers in cooperation.
Optionally, the main body of execution of each step of the method may include the following cases:
in the first case: the blind node is used for positioning, the first server stores a channel model formula, and the channel model formula is corrected; that is, the first server is used as the correction server.
Referring to fig. 4, when the blind node is to be located, a parameter request is sent to the first server, the first server returns a parameter response to the blind node, the parameter response carries at least one parameter of the channel model formula, the blind node can determine the channel model formula according to the obtained parameter, and the blind node is located according to the channel model formula to determine the position of the blind node.
And then, the blind node can send the positioning result data to the first server, and the first server can collect multiple groups of positioning result data and calculate the corrected parameters according to the multiple groups of positioning result data, so that the channel model formula is corrected.
The parameter obtaining request may carry information such as area information, sub-area information, and parameter type of the blind node, which is not limited in the embodiment of the present invention.
The parameter type indicates the type of the parameter to be acquired, such as reference signal strength, channel attenuation parameter, and the like.
The area information may be country ID (Identity, serial number) + province ID + city ID + area (street, town, etc.) ID + building ID. Or, the parameter acquisition request may not carry the area information. For example, when the parameter acquisition request needs to be forwarded through a gateway of an area where the blind node is located, the first server may determine the area where the blind node is located according to a communication gateway identifier, and at this time, the parameter acquisition request does not need to carry the area information. Or different first servers are set for different areas, and when one first server is only responsible for one area, the parameter acquisition request does not need to carry the area information.
The sub-region information may be information such as a coordinate range of a blind node, a coordinate point, an anchor node identifier, and the like, which is not limited in the embodiment of the present invention. If different channel models are set for different sub-regions or anchor nodes, the corresponding channel models can be determined according to the sub-region information.
In the second case: the blind node is used for positioning, the first server is used for correcting the channel model formula, and the second server is used for storing the channel model formula. That is, the first server is used as the correction server.
Referring to fig. 5, when the blind node is to be located, a parameter request is sent to the second server, the second server returns a parameter response to the blind node, the parameter response carries at least one parameter of the channel model formula, the blind node can determine the channel model formula according to the obtained parameter, and the blind node is located according to the channel model formula to determine the position of the blind node.
Then, the blind node may send the positioning result data to the first server, and the first server may collect a plurality of sets of positioning result data and calculate the corrected parameters according to the plurality of sets of positioning result data. The second server can send a parameter updating request to the first server in real time or periodically, the first server returns a parameter updating response to the second server, the parameter updating response carries the modified parameters, the second server can update the parameters and store the modified parameters, and therefore the modified parameters can be sent to the blind node to be positioned later.
The first server may calculate only the corrected parameters, send the parameters to the second server, and perform filtering and smoothing processing by the second server.
In the third case: the second server performs the positioning, and the first server stores the channel model formula and performs the correction. That is, the first server is used as the correction server, and the second server is used as the positioning server.
Referring to fig. 6, the third case is similar to the first case, except that the second server determines the location of the blind node and requests the first server for the modified parameters, which is not described herein again.
In a fourth case: and positioning by the second server, collecting positioning result data by the first server, storing a channel model formula by the second server, and correcting according to the positioning result data. That is, the second server is used as a positioning server, and the first server and the second server are used as correction servers, wherein the first server is used for collecting data, and the second server is used for correcting.
Referring to fig. 7, the second server locates the blind node according to the stored parameters to obtain location result data, and sends the location result data to the first server. After the first server collects the multiple groups of positioning result data, the positioning result data are sent to the second server, and the second server can correct the positioning result data according to the multiple groups of positioning result data. And then, positioning the blind node according to the corrected channel model formula.
In the fifth case: and positioning by the second server, storing the channel model formula by the second server, collecting positioning result data, and correcting the channel model formula. That is, the first server is used as the positioning server, and the second server is used as the correction server.
The first server and the second server provided by the embodiment of the invention are positioned in the same server. Specifically, the server may include a plurality of functional modules, such as a positioning module for performing position estimation, a data module for collecting positioning result data, and a correction module for performing correction, where the data module may transmit the positioning result data to the correction module, and after the correction module obtains a new channel model formula by correction, the positioning module may perform positioning according to the channel model formula corrected by the correction module.
Of course, in addition to the above cases, other cases of execution subjects may be adopted to execute the steps provided by the embodiment of the present invention, and the embodiment of the present invention is not limited thereto.
In practical application, the off-line measurement can be performed again to correct the channel model formula, but the off-line measurement has a large workload, which increases the maintenance cost, if the off-line measurement is performed too frequently, the maintenance cost of the positioning system will be greatly increased, and if the time interval for performing the off-line measurement is long, the positioning accuracy will be reduced.
Alternatively, measurement nodes may be deployed at known locations, and the measurement nodes may perform measurements, and the measured data may be used to modify the channel model equation. But deploying measurement nodes can greatly increase the construction cost. If the number of the measurement nodes is too large, the increased construction cost is very high, and if the number of the measurement nodes is too small, the correction is inaccurate, and the positioning precision is reduced.
Compared with the scheme, the method provided by the embodiment of the invention can solve the problem that the effectiveness of the channel model formula is reduced along with the change of the environment, can accurately update the channel model formula, avoids increasing too much construction cost and maintenance cost for the positioning system, and obviously improves the positioning performance.
Fig. 8 is a schematic structural diagram of a channel model formula correction device according to an embodiment of the present invention, and referring to fig. 8, the channel model formula correction device includes: a receiver 801, a transmitter 802, a memory 803 and a processor 804, wherein the receiver 801, the transmitter 802 and the memory 803 are respectively connected to the processor 804, the memory 803 stores a program code, and the processor 804 is configured to call the program code to perform the following operations:
collecting m groups of positioning result data, wherein the ith group of positioning result data comprises a node Nu，iAnd node Nv，iIn betweenDistance diAnd signal propagation characteristic Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relation between signal propagation characteristics and distances, i represents the positioning times, i is 1,2.
And correcting the channel model formula according to the distance and the signal propagation characteristics in the m groups of positioning result data to obtain a corrected channel model formula.
In a first possible implementation, the channel model formula modifying device is a user equipment or a server. That is, the channel model formula correction method provided in the embodiment of the present invention may be executed by the user equipment performing the positioning, or may be executed by any server, which is not limited in the embodiment of the present invention.
In a second possible implementation, the processor 804 is further configured to call the program code to perform the following operations:
d sent by the positioning server is received by the receiver 801iAnd Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node N via receiver 801v，iTransmitted diAnd Pi。
In a third possible implementation manner, the processor 804 is further configured to invoke the program code to perform the following operations through the receiver 801:
receiving node Nv，iTransmitted location information and PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe sent position information and the P sent by the positioning server are receivediThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iHejie (Chinese character)Point Nv，iA distance d betweeni(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iPosition information and P ofiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nv，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nu，iTransmitted PiThe position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
In a fourth possible implementation, the channel model is formulated as
Figure BDA0000973834700000311
The signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing nodesN1，0And node N2，0With reference signal strength therebetween, n represents a channel attenuation parameter,
Figure BDA0000973834700000312
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the processor 804 is further configured to invoke the program code to perform the following operations:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000313
And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000314
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000321
according to P0', n ', and sigma ' to obtain a modified channel model formula
Figure BDA0000973834700000322
Figure BDA0000973834700000323
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、n、σ、P0’、n 'and sigma' are smoothed to obtain P0", n", and σ ", according to P0The channel model formula after the correction is obtained by the ' n ' and the ' sigma
Figure BDA0000973834700000324
Figure BDA0000973834700000325
In a fifth possible implementation, the channel model is formulated as
Figure BDA0000973834700000326
The signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1，0And node N2，0Reference distance between, P0Representing a node N1，0And node N2，0With reference signal strength, alpha represents a channel attenuation parameter,
Figure BDA0000973834700000327
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the processor 804 is further configured to invoke the program code to perform the following operations:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure BDA0000973834700000328
And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000331
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000332
according to P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure BDA0000973834700000333
Figure BDA0000973834700000334
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure BDA0000973834700000335
Figure BDA0000973834700000336
In a sixth possible implementation, the channel model is formulated as
Figure BDA0000973834700000337
The signal propagation characteristic PiAs signal propagation time Pti；
Wherein a represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd node NvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure BDA0000973834700000338
sigma denotes the mean square of XA difference;
the processor 804 is further configured to invoke the program code to perform the following operations:
calculating a corrected first weighting parameter a' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000339
and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000341
and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure BDA0000973834700000342
obtaining a corrected channel model formula according to a ', b' and sigma
Figure BDA0000973834700000343
Figure BDA0000973834700000344
Alternatively, the first and second electrodes may be,
smoothing the a, b, sigma, a ', b' and sigma 'by adopting a filtering smoothing algorithm to obtain a', b 'and sigma', and obtaining a corrected channel model formula according to the a ', b' and sigma
Figure BDA0000973834700000345
Figure BDA0000973834700000346
In a seventh possible implementation, the filter smoothing algorithm is S ═ x × S + (1-x) × S';
wherein S represents an original parameter, S ' represents a correction parameter obtained by correcting S, S ' represents a parameter obtained by smoothing S and S ', x represents a weighting coefficient, and x is a number in a range of [0, 1);
alternatively, the filter smoothing algorithm is
Figure BDA0000973834700000347
Wherein j represents the number of correction times, S represents the original parameter, S (j-i + 1)' represents the correction parameter obtained after correcting S at the j-i +1 th time, S (j) represents the parameter obtained after smoothing processing at the j-th correction time, x represents the weighting coefficient, x represents the weight coefficient, and x represents the weight coefficientiIs a number in the range of [0, 1), and
Figure BDA0000973834700000348
in this embodiment of the present invention, the processor 804 may include a collecting module and a modifying module, where the collecting module is configured to collect m sets of positioning result data, and the modifying module is configured to modify the channel model formula according to a distance and a signal propagation characteristic in the m sets of positioning result data, so as to obtain a modified channel model formula.
Wherein the channel model formula is aimed at
Figure BDA0000973834700000351
The correction module may include a first correction unit, a second correction unit, and a third correction unit. The first correcting unit is used for correcting the reference signal intensity P0The second correcting unit is used for correcting the channel attenuation parameter n, and the third correcting unit is used for correcting the mean square error sigma.
Formula for channel model
Figure BDA0000973834700000352
The correction module may include a fourth correction unit, a fifth correction unit, and a sixth correction unit. A fourth correction unit for correcting the reference signal strength P0The fifth modification unit is used for modifying the channel attenuation parameter alpha, and the sixth modification unit is used for modifying the mean square error sigma.
Formula for channel model
Figure BDA0000973834700000353
The correction module may include a seventh correction unit, an eighth correction unit, and a ninth correction unit. The seventh modification unit is used for modifying the first weighting parameter a, the eighth modification unit is used for modifying the second weighting parameter b, and the ninth modification unit is used for modifying the mean square error sigma.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (14)

1. A method for modifying a channel model equation, the method comprising:
collecting m groups of positioning result data, wherein for the ith group of positioning result data in the m groups of positioning result data, receiving a node N sent by a positioning serveru，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi(ii) a Or, the receiving node Nv，iTransmitted diAnd Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the incidence relation between the signal propagation characteristics and the distance, the channel model formula comprises at least one parameter, a distance variable and a signal propagation characteristic variable, and the at least one parameter comprises the reference signal strength orAt least one of reference signal propagation times, i represents a positioning time, i ═ 1,2.. m, u and v represent node numbers;
and taking the m groups of positioning result data as known quantity, deducing a formula for calculating parameters according to the channel model formula, and calculating at least one new parameter in the channel model formula based on the m groups of positioning result data and the deduced formula to obtain a corrected channel model formula.
2. The method of claim 1, wherein said collecting m sets of positioning result data comprises:
receiving node Nv，iTransmitted location information and Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe sent position information and the P sent by the positioning server are receivedi(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iPosition information and P ofi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nv，iTransmitted Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nu，iTransmitted Pi；
The position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
3. The method of claim 1, wherein the channel model is formulated as
Figure FDA0002813286270000021
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1,0And node N2,0Reference distance between, P0Representing a node N1,0And node N2,0With reference signal strength therebetween, n represents a channel attenuation parameter,
Figure FDA0002813286270000022
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the deriving a formula for calculating parameters according to the channel model formula using the m sets of positioning result data as known quantities, and calculating at least one new parameter in the channel model formula based on the m sets of positioning result data and the derived formula to obtain a modified channel model formula, including:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure FDA0002813286270000023
And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000024
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000025
according to P0', n ', and sigma ' to obtain a modified channel model formula
Figure FDA0002813286270000026
Figure FDA0002813286270000031
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、n、σ、P0', n ' and sigma ' are smoothed to obtain P0", n", and σ ", according to P0The channel model formula after the correction is obtained by the ' n ' and the ' sigma
Figure FDA0002813286270000032
Figure FDA0002813286270000033
4. The method of claim 1, wherein the channel model is formulated as
Figure FDA0002813286270000034
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1,0And node N2,0Reference distance between, P0Representing a node N1,0And node N2,0With reference signal strength, alpha represents a channel attenuation parameter,
Figure FDA0002813286270000035
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the deriving a formula for calculating parameters according to the channel model formula using the m sets of positioning result data as known quantities, and calculating at least one new parameter in the channel model formula based on the m sets of positioning result data and the derived formula to obtain a modified channel model formula, including:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure FDA0002813286270000036
And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000041
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000042
according to P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure FDA0002813286270000043
Figure FDA0002813286270000044
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure FDA0002813286270000045
5. The method of claim 1, wherein the channel model is formulated as
Figure FDA0002813286270000046
Said signal propagation characteristic PiAs signal propagation time Pti；
Wherein a represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd node NvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure FDA0002813286270000047
σ represents the mean square error of X;
the deriving a formula for calculating parameters according to the channel model formula using the m sets of positioning result data as known quantities, and calculating at least one new parameter in the channel model formula based on the m sets of positioning result data and the derived formula to obtain a modified channel model formula, including:
calculating a corrected first weighting parameter a' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000051
and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000052
and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000053
obtaining a corrected channel model formula according to a ', b' and sigma
Figure FDA0002813286270000054
Figure FDA0002813286270000055
Alternatively, the first and second electrodes may be,
smoothing the a, b, sigma, a ', b' and sigma 'by adopting a filtering smoothing algorithm to obtain a', b 'and sigma', and obtaining a corrected channel model formula according to the a ', b' and sigma
Figure FDA0002813286270000056
Figure FDA0002813286270000057
6. The method according to any one of claims 3 to 5,
the filtering smoothing algorithm is S ═ x S + (1-x) × S';
wherein S represents an original parameter, S ' represents a correction parameter obtained by correcting S, S ' represents a parameter obtained by smoothing S and S ', x represents a weighting coefficient, and x is a number in a range of [0, 1);
alternatively, the filter smoothing algorithm is
Figure FDA0002813286270000058
Wherein j represents the number of corrections, S represents the original parameter, and S (j-i + 1)' tableThe correction parameters obtained by correcting S at the j-i +1 th time, S (j) represents the parameters obtained by smoothing at the j-th time, x represents the weighting coefficient, xiIs a number in the range of [0, 1), and
Figure FDA0002813286270000061
7. an apparatus for modifying a channel model equation, the apparatus comprising:
a collecting module, configured to collect m groups of positioning result data, where for an ith group of positioning result data in the m groups of positioning result data, a node N sent by a positioning server is receivedu，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi(ii) a Or, the receiving node Nv，iTransmitted diAnd Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relationship between the signal propagation characteristics and the distance, the channel model formula comprises at least one parameter, a distance variable and a signal propagation characteristic variable, the at least one parameter comprises at least one of reference signal strength or reference signal propagation time, i represents the positioning times, i is 1,2.
And the correction module is used for deducing a formula for calculating parameters according to the channel model formula by taking the m groups of positioning result data as known quantity, and calculating at least one new parameter in the channel model formula based on the m groups of positioning result data and the deduced formula to obtain a corrected channel model formula.
8. The apparatus of claim 7, wherein the collection module is specifically configured to:
receiving node Nv，iTransmitted location information and Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe sent position information and the P sent by the positioning server are receivedi(ii) a Alternatively, the first and second electrodes may be,
receiving node Nv，iThe transmitted location information, and the receiving node Nu，iTransmitted Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iPosition information and P ofi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nv，iTransmitted Pi(ii) a Alternatively, the first and second electrodes may be,
receiving node N sent by positioning serverv，iAnd receive node Nu，iTransmitted Pi；
The position information is used for indicating the node Nv，iAccording to the position of node Nu，iPosition and node Nv，iPosition of, compute node Nu，iAnd node Nv，iA distance d betweeni。
9. The apparatus of claim 7, wherein the channel model is formulated as
Figure FDA0002813286270000071
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1,0And node N2,0Reference distance between, P0Representing a node N1,0And node N2,0With reference signal strength therebetween, n represents a channel attenuation parameter,
Figure FDA0002813286270000072
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the correction module is used for:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure FDA0002813286270000073
And calculating the corrected channel attenuation parameter n' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000074
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000075
according to P0', n ', and sigma ' to obtain a modified channel model formula
Figure FDA0002813286270000076
Figure FDA0002813286270000077
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、n、σ、P0', n ' and sigma ' are smoothed to obtain P0", n", and σ ", according to P0The channel model formula after the correction is obtained by the ' n ' and the ' sigma
Figure FDA0002813286270000081
Figure FDA0002813286270000082
10. The apparatus of claim 7, wherein the channel model is formulated as
Figure FDA0002813286270000083
Said signal propagation characteristic PiIs the signal strength Pri；
Wherein d is0Indicating measured node N1,0And node N2,0Reference distance between, P0Representing a node N1,0And node N2,0With reference signal strength, alpha represents a channel attenuation parameter,
Figure FDA0002813286270000084
σ denotes the mean square error of X, and the variable d denotes the node NuAnd node NvThe distance between, variable PrRepresenting a node NuAnd node NvSignal strength in between;
the correction module is used for:
calculating the corrected reference signal intensity P according to the distance and the signal intensity in the m groups of positioning result data by adopting the following formula0’：
Figure FDA0002813286270000085
And calculating the corrected channel attenuation parameter alpha' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000086
and calculating the corrected mean square error sigma' according to the distance and the signal strength in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000091
according to P0', alpha ' and sigma ' to obtain the corrected channel model formula
Figure FDA0002813286270000092
Figure FDA0002813286270000093
Alternatively, the first and second electrodes may be,
using smoothing algorithm for P0、α、σ、P0', alpha ' and sigma ' are smoothed to obtain P0", α", and σ ", according to P0The channel model formula after the correction of the 'alpha' and the 'sigma' is obtained
Figure FDA0002813286270000094
11. The apparatus of claim 7, wherein the channel model is formulated as
Figure FDA0002813286270000095
Said signal propagation characteristic PiAs signal propagation time Pti；
Wherein a represents a first weighting parameter, b represents a second weighting parameter, and the variable d represents the node NuAnd node NvC is the electromagnetic wave velocity, variable PtRepresenting a node NuAnd node NvThe time of propagation of the signal in between,
Figure FDA0002813286270000096
σ represents the mean square error of X;
the correction module is used for:
calculating a corrected first weighting parameter a' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000097
and calculating a corrected second weighting parameter b' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000098
and calculating the corrected mean square error sigma' according to the distance and the signal propagation time in the m groups of positioning result data by adopting the following formula:
Figure FDA0002813286270000101
obtaining a corrected channel model formula according to a ', b' and sigma
Figure FDA0002813286270000102
Figure FDA0002813286270000103
Alternatively, the first and second electrodes may be,
smoothing the a, b, sigma, a ', b' and sigma 'by adopting a filtering smoothing algorithm to obtain a', b 'and sigma', and obtaining a corrected channel model formula according to the a ', b' and sigma
Figure FDA0002813286270000104
Figure FDA0002813286270000105
12. The apparatus according to any of claims 9-11, wherein the filter smoothing algorithm is S "═ x S + (1-x) × S';
wherein S represents an original parameter, S ' represents a correction parameter obtained by correcting S, S ' represents a parameter obtained by smoothing S and S ', x represents a weighting coefficient, and x is a number in a range of [0, 1);
alternatively, the filter smoothing algorithm is
Figure FDA0002813286270000106
Wherein j represents the number of correction times, S represents the original parameter, S (j-i + 1)' represents the correction parameter obtained after correcting S at the j-i +1 th time, S (j) represents the parameter obtained after smoothing processing at the j-th correction time, x represents the weighting coefficient, x represents the weight coefficient, and x represents the weight coefficientiIs a number in the range of [0, 1), and
Figure FDA0002813286270000107
13. a channel model equation modification apparatus, characterized in that the channel model equation modification apparatus comprises: the receiver, the transmitter, the memory and the processor are respectively connected with the processor, the memory stores program codes, and the processor is used for calling the program codes and executing the following operations:
collecting m groups of positioning result data, wherein for the ith group of positioning result data in the m groups of positioning result data, receiving a node N sent by a positioning serveru，iAnd node Nv，iA distance d betweeniAnd signal propagation characteristic Pi(ii) a Or, the receiving node Nv，iTransmitted diAnd Pi，diAnd PiIs based on the channel model formula and node Nu，iPosition of (2) to node Nv，iThe channel model formula is obtained after positioning, the channel model formula is used for representing the association relationship between the signal propagation characteristics and the distance, the channel model formula comprises at least one parameter, a distance variable and a signal propagation characteristic variable, the at least one parameter comprises at least one of reference signal strength or reference signal propagation time, i represents the positioning times, i is 1,2.
And taking the m groups of positioning result data as known quantity, deducing a formula for calculating parameters according to the channel model formula, and calculating at least one new parameter in the channel model formula based on the m groups of positioning result data and the deduced formula to obtain a corrected channel model formula.
14. The apparatus of claim 13, wherein the channel model formula modifying apparatus is a user equipment or a server.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1951027A (en) * 2004-04-30 2007-04-18 香港应用科技研究院有限公司 Location determination and location tracking in wireless networks
US8811189B2 (en) * 2012-04-23 2014-08-19 Cisco Technology, Inc. Generating accurate dynamic heat maps
CN104025677A (en) * 2011-08-18 2014-09-03 里瓦达研究公司 Method and System for Providing Enhanced Location Based Information for Wireless Handsets
CN105072581A (en) * 2015-08-26 2015-11-18 四川星网云联科技有限公司 Indoor positioning method of path attenuation coefficient based database construction

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7853267B2 (en) * 2000-07-10 2010-12-14 Andrew Llc Wireless system signal propagation collection and analysis
US6990428B1 (en) * 2003-07-28 2006-01-24 Cisco Technology, Inc. Radiolocation using path loss data
EP2584371B1 (en) * 2011-10-17 2016-12-21 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Range estimation method based on RSS measurement with limited sensitivity receiver
CN106209281B (en) * 2016-06-23 2018-08-17 广州大学 Wireless sensor network RSSI attenuation models calibrating installation and method on the spot

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1951027A (en) * 2004-04-30 2007-04-18 香港应用科技研究院有限公司 Location determination and location tracking in wireless networks
CN104025677A (en) * 2011-08-18 2014-09-03 里瓦达研究公司 Method and System for Providing Enhanced Location Based Information for Wireless Handsets
US8811189B2 (en) * 2012-04-23 2014-08-19 Cisco Technology, Inc. Generating accurate dynamic heat maps
CN105072581A (en) * 2015-08-26 2015-11-18 四川星网云联科技有限公司 Indoor positioning method of path attenuation coefficient based database construction

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Comparing Ray Tracing, Free Space Path Loss and Logarithmic Distance Path Loss Models in Success of Indoor Localization with RSSI;Ozkan Katirci等;《19th Telecommunications forum TELFOR 2011》;20111124;第313-316页 *
一种通用室内链路损耗经验模型的修正;刘韬等;《网络与通信》;20151231;第34卷(第6期);第69-71页 *
基于RSSI 的对数距离路径损耗模型研究;申静涛;《电子质量》;20131231(第12期);第15-17页 *

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