CN108549049B - Ray tracing assisted Bayes fingerprint positioning method and device - Google Patents

Ray tracing assisted Bayes fingerprint positioning method and device Download PDF

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CN108549049B
CN108549049B CN201810326517.8A CN201810326517A CN108549049B CN 108549049 B CN108549049 B CN 108549049B CN 201810326517 A CN201810326517 A CN 201810326517A CN 108549049 B CN108549049 B CN 108549049B
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point
signal strength
probability
reference point
signal intensity
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CN108549049A (en
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邓中亮
王翰华
付潇
姚喆
刘雯
李晶
冷泽富
邢华帅
焦继超
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Beijing University of Posts and Telecommunications
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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
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • 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
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

The invention provides a ray tracing assisted Bayes fingerprint positioning method and device. The method comprises the following steps: calculating the reference signal intensity of each AP at each reference point according to the transmitting power of each AP in the area to be positioned and a preset ray tracing algorithm; for each AP, calculating a signal intensity probability distribution model of the AP according to a pre-acquired composition parameter of a Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula; and when a positioning request which is sent by a terminal and carries the detection signal strength of each AP at the position to be positioned is received, determining the position of the position to be positioned by a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP. By adopting the invention, the manpower consumption and the time consumption required by positioning can be reduced.

Description

Ray tracing assisted Bayes fingerprint positioning method and device
Technical Field
The present application relates to the field of communications technologies, and in particular, to a ray tracing assisted bayesian fingerprint positioning method and apparatus.
Background
Indoor location services have received extensive attention and research due to their enormous social and economic potential. The market for indoor location services is predicted to reach $ 100 billion by 2020. Nowadays, the technology development of the global positioning system mainly based on the global positioning system and the big dipper is more mature, and the precise outdoor position service can be provided. However, in the valley, the dense urban building area and the indoor environment, the propagation of the satellite positioning signal is obstructed by the obstruction of the obstacle, and the satellite positioning system cannot obtain an accurate positioning result in these environments. In order to solve the problem of accurate positioning in these scenarios, a variety of indoor positioning technologies based on Wireless signals are proposed in succession, and mainly use Wireless technologies such as Wi-Fi (Wireless Fidelity, Wireless local area network based on ieee802.11b standard), bluetooth, and ultra wide band. In which, the technology popularity, the layout cost of the positioning environment and the positioning accuracy are considered comprehensively, and the Wi-Fi and bluetooth positioning technologies have been widely commercially applied because of their high technology popularity, low layout cost and acceptable positioning accuracy.
Wi-Fi and Bluetooth positioning typically use a fingerprint matching algorithm based on Received Signal Strength (RSS) for positioning. Fingerprint matching algorithm: collecting RSS data at each reference point in an area to be positioned, and storing the collected RSS data and coordinates of each reference point corresponding to the RSS data into a fingerprint database; when receiving a positioning request which is sent by a terminal and carries the RSS at the position to be positioned, the server matches the RSS at the position to be positioned with the fingerprint database to obtain a positioning result. Based on this positioning method, many pairs of reference points need to be set in the region to be positioned, and a large amount of data needs to be collected at each reference point, which consumes a lot of time and manpower.
Disclosure of Invention
An object of the embodiments of the present application is to provide a ray tracing assisted bayesian fingerprint positioning method and device, so as to reduce the labor consumption and time consumption required for positioning. The specific technical scheme is as follows:
in a first aspect, a ray tracing assisted bayesian fingerprint positioning method is provided, the method comprising:
calculating the reference signal intensity of each AP at each reference point in the area to be positioned according to the transmitting power of each AP in the area to be positioned and a preset ray tracing algorithm;
for each AP, calculating a signal intensity probability distribution model of the AP according to a pre-acquired composition parameter of a Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula, wherein the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point;
and when a positioning request which is sent by a terminal and carries the detection signal strength of each AP at the point to be positioned is received, determining the position of the point to be positioned through a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the point to be positioned.
Optionally, the calculating, according to the transmission power of each AP in the area to be located and a preset ray tracing algorithm, the reference signal strength of each AP at each reference point in the area to be located includes:
aiming at each AP, determining the signal strength sent by the AP according to the transmitting power of the AP;
calculating the signal intensity loss of the signal sent by the AP at each reference point according to a wireless signal path loss calculation formula and the corresponding obstacle distribution data at each reference point;
and determining the signal strength of the AP at each reference point according to the signal strength sent by the AP and the signal strength loss of the signal sent by the AP at each reference point.
Optionally, the method further includes:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
for each AP, determining the composition parameters of a Gaussian mixture model of the AP at the preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point.
Optionally, the determining, according to the signal intensity probability distribution model of each AP and the detected signal intensity of each AP at a point to be positioned, the position of the point to be positioned by using a bayesian algorithm includes:
aiming at each AP, calculating the prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to the signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
determining the probability that each reference point is a point to be located according to the reference probability that each reference point is a point to be located;
and determining the reference point with the maximum probability as the point to be located.
Optionally, the determining, according to the reference probability that each reference point is a point to be located, the probability that each reference point is a point to be located includes:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
In a second aspect, a ray tracing assisted bayesian fingerprint locating device is provided, the device comprising:
the first calculation module is used for calculating through a ray tracing method according to the transmitting power of each wireless Access Point (AP) in the area to be positioned to obtain the reference signal intensity of each AP at each reference point in the area to be positioned;
the second calculation module is used for calculating a signal intensity probability distribution model of each AP according to a pre-acquired composition parameter of the Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point;
and the positioning module is used for determining the position of the to-be-positioned point according to the signal intensity probability distribution model of each AP and the detection signal intensity of each AP at the to-be-positioned point by a Bayesian algorithm when receiving a positioning request which is sent by a terminal and carries the detection signal intensity of each AP at the to-be-positioned point.
Optionally, the first computing module includes:
the first calculating unit is used for determining the signal strength sent by each AP according to the transmitting power of the AP;
a second calculating unit, configured to calculate, according to a wireless signal path loss calculation formula and the corresponding obstacle distribution data at each reference point, a signal strength loss of the signal sent by the AP at each reference point;
and the third calculating unit is used for determining the signal strength of the AP at each reference point according to the signal strength sent by the AP and the signal strength loss of the signal sent by the AP at each reference point.
Optionally, the apparatus further includes a gaussian mixture model calculation module, where the gaussian mixture model calculation module is specifically configured to:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
for each AP, determining the composition parameters of a Gaussian mixture model of the AP at the preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point.
Optionally, the positioning module includes:
a first positioning calculation unit, configured to calculate, for each AP, a prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to a signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
the second positioning calculation unit is used for determining the probability that each reference point is a point to be positioned according to the reference probability that each reference point is a point to be positioned;
and the positioning point determining unit is used for determining the reference point with the maximum probability as the to-be-positioned point.
Optionally, the second positioning calculation unit is specifically configured to:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
According to the ray tracing assisted Bayes fingerprint positioning method provided by the embodiment of the invention, Bayes fingerprint matching positioning can be realized by utilizing a ray tracing method and a small amount of data acquisition statistics, and manpower consumption and time consumption required by positioning can be effectively reduced.
Of course, it is not necessary for any product or method of the present application to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a ray tracing-assisted bayesian fingerprint positioning method according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for determining the strength of a reference signal at each reference point in an area to be located according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for obtaining composition parameters of a gaussian mixture model of each AP at a preset collection point according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for determining a position of a location point according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a ray tracing-assisted bayesian fingerprint positioning apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. 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 application.
The embodiment of the invention also provides a ray tracing assisted Bayes fingerprint positioning method, which is applied to the server. In practical applications, a location service provider will usually set up a plurality of routers indoors, i.e. provide a plurality of access points AP. And the server calculates the signal intensity probability distribution model of each AP according to the pre-acquired composition parameters of the Gaussian mixture model of the AP at the preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula. The method for acquiring the composition parameters of the Gaussian mixture model of each AP at the preset acquisition point comprises the following steps: the terminal samples the signal intensity of each AP within a preset time period at an indoor acquisition point; the method comprises the steps that a server obtains signal intensity data sampled by a terminal, and for each AP, the server determines composition parameters of a Gaussian mixture model of the AP at a preset acquisition point according to the obtained sampling data of the AP signal intensity, a Gaussian mixture model formula and a preset estimation algorithm. Wherein, the Gaussian mixture model represents the probability distribution of the signal intensity of the AP at the preset acquisition point. And when the terminal needs to be positioned, the terminal sends a positioning request carrying the detection signal strength of each AP at the point to be positioned to the server. When the server receives a positioning request which is sent by the terminal and carries the detection signal strength of each AP at a point to be positioned, the server determines the position of the point to be positioned through a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the point to be positioned.
As shown in fig. 1, the method may include the steps of:
step 101, calculating the reference signal intensity of each AP at each reference point in the area to be positioned according to the transmitting power of each wireless access point AP in the area to be positioned and a preset ray tracing algorithm.
In implementation, for each AP, the position of the AP may be used as an origin, and a signal emitted by the AP may be regarded as a plurality of ray signals emitted from the origin, when each ray signal passes through an obstacle, a part of the signal may pass through the obstacle and a part of the signal may be reflected by the obstacle, directions of the transmitted and reflected ray signals may be obtained according to a mirror principle, and when the ray signal passes through the obstacle, the signal intensity may be lost.
The signal intensity loss of the radiation signal transmitted through the obstacle is equal to the signal attenuation rate of the obstacle multiplied by the thickness of the obstacle, wherein the signal attenuation rate of the obstacle can be calculated by formula (1).
Figure BDA0001626753180000061
Wherein A is the signal attenuation rate of the obstacle, σ is the permittivity of the obstacle,ris the relative dielectric constant of the obstruction.
The signal intensity loss of the radiation signal after being reflected by the obstacle is equal to the reflection coefficient of the obstacle multiplied by the signal intensity when the radiation signal is incident, wherein the reflection coefficient of the obstacle can be obtained through a formula (2), a formula (3) and a formula (4).
Figure BDA0001626753180000062
Figure BDA0001626753180000063
Figure BDA0001626753180000064
Wherein η represents complex dielectric constant of the obstacle, incident direction of theta line signal and surface of the obstacleAngle of normal vector, RCIs the reflection coefficient of the obstacle.
Based on the above, for each AP, the server obtains the position of the AP, determines an obstacle through which a radiation signal emitted by the AP reaches each reference point according to distribution information of the obstacle, determines the signal intensity of each radiation signal according to the transmission power of the AP, calculates the signal intensity loss of each radiation signal reaching each reference point according to the permittivity, relative dielectric constant, and complex dielectric function of the obstacle through which each radiation signal reaches each reference point, and subtracts the signal intensity loss from the signal intensity of each radiation signal reaching each reference point to obtain the reference signal intensity of each reference point.
Optionally, referring to fig. 2, the reference signal strength of each AP at each reference point in the area to be located is obtained through ray tracing calculation according to the transmission power of each AP in the area to be located, and the specific processing steps are as follows:
step 201, for each AP, determining the signal strength sent by the AP according to the transmission power of the AP.
In implementation, for each AP, the server obtains the AP location, and determines the signal strength of the ray signal emitted from the AP location by the AP according to the transmission power of the AP.
Step 202, calculating a signal strength loss of the signal sent by the AP at each reference point according to a wireless signal path loss calculation formula and corresponding obstacle distribution data at each reference point.
In the implementation, for each AP, the server determines, from the distribution data of the obstacles, the radiation signal reaching each reference point and the obstacle through which the radiation signal passes in the radiation signal emitted by the AP, and calculates, from the signal intensity of the radiation signal reaching each reference point, the permittivity, relative permittivity, and complex permittivity of the obstacle through which the radiation signal reaching each reference point passes, the signal intensity loss of the radiation signal reaching each reference point by the above formulas (1) to (4).
And step 203, determining the signal strength of the AP at each reference point according to the signal strength of the AP and the signal strength loss of the signal sent by the AP at each reference point.
In implementation, the server subtracts the signal intensity loss of the ray from the signal intensity of the ray signal reaching each reference point when the ray signal is emitted, so as to obtain the reference signal intensity of each reference point.
And 102, aiming at each AP, calculating a signal intensity probability distribution model of the AP according to a pre-acquired composition parameter of the Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula.
Wherein, the Gaussian mixture model represents the probability distribution of the signal intensity of the AP at the preset acquisition point.
In implementation, for each AP, the server obtains, according to a formula (5), a signal intensity distribution model of the AP according to a pre-obtained number of gaussian models of a gaussian mixture model of the AP at a preset acquisition point, a weight of each gaussian model, a mean and a variance of each gaussian model, and a reference signal intensity at each reference point.
Figure BDA0001626753180000081
Wherein the RSS0For the reference signal strength of the AP at the reference point (x, y), Q is the number of Gaussian models, σmax 2Variance value of Gaussian model with largest weight, αmaxIs the weight value of the Gaussian model with the largest weight, q is the Gaussian model except the Gaussian model with the largest weight, sigmaq 2Variance value of Gaussian model q, αqIs the weight value of the Gaussian model q, muqThe expected value of the Gaussian model q, P (RSS | (x, y)) is the signal strength distribution model of the AP, and dqIs mumaxDifference from the mean of the Gaussian model q, μmaxThe expectation value of the Gaussian model with the largest weight is obtained.
Optionally, referring to fig. 3, the composition parameters of the gaussian mixture model of each AP at the preset acquisition point are obtained, and the specific processing steps are as follows:
step 301, acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time period.
In implementation, for each AP, the terminal samples the signal strength of the AP at a preset acquisition point to obtain a set of signal strength data, e.g., { RSS }1,…,RSSN}。
302, aiming at each AP, determining the composition parameters of a Gaussian mixture model of the AP at a preset acquisition point according to the acquired sampling data of the AP signal intensity, a Gaussian mixture model formula and a preset estimation algorithm; the gaussian mixture model represents the probability distribution of the signal strength of the AP at a preset acquisition point.
In implementation, the server acquires signal intensity data sampled by the terminal, and uses a Gaussian mixture model formula (6) to fit the acquired signal intensity data to estimate composition parameters of the Gaussian mixture model formula (6).
Figure BDA0001626753180000082
Wherein Q is the number of Gaussian models, sigmaqStandard deviation of Gaussian model q, αqIs the weight value of the Gaussian model q, muqFor the expected value of the Gaussian model q, P (RSS) is the Gaussian mixture model of the AP at the preset acquisition point.
And 103, when a positioning request which is sent by the terminal and carries the detection signal strength of each AP at the point to be positioned is received, determining the position of the point to be positioned through a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the point to be positioned.
In implementation, when a server receives a positioning request which is sent by a terminal and carries the detection signal strength of each AP at a to-be-positioned point, aiming at each AP, the server calculates the probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to a signal strength probability distribution model of the AP and the detection signal strength of the AP at the to-be-positioned point, the calculated probability is used as prior probability, and then the probability that each reference point is the to-be-positioned point is calculated according to the prior probability and a Bayesian rule calculation formula. And then, the server determines the probability that each reference point is the point to be located according to the probability that each reference point corresponding to the detection signal strength of each AP is the point to be located, and takes the reference point with the maximum probability as the point to be located.
Optionally, referring to fig. 4, the position of the to-be-positioned point is determined through a bayesian algorithm according to the signal intensity probability distribution model of each AP and the detection signal intensity of each AP at the to-be-positioned point, and the specific processing steps are as follows:
step 401, for each AP, calculating, according to the signal strength probability distribution model of the AP and the detected signal strength of the AP at a point to be located, a prior probability that the signal strength of the AP at each reference point is the detected signal strength of the AP; and calculating the reference probability of each reference point as the point to be positioned according to a Bayesian rule probability formula and each calculated prior probability.
In implementation, for each AP, the server calculates, according to the signal strength probability distribution model of the AP and the detected signal strength of the AP at the point to be located, a prior probability that the signal strength of the AP at each reference point is the detected signal strength of the AP, and calculates, by using formula (7), a reference probability that each reference point corresponding to the detected signal strength of the AP is the point to be located.
Figure BDA0001626753180000091
Wherein S is a region to be positioned, (x)r,yr) As a reference point, RSSiIs APiThe intensity of the detected signal at the point to be located, P ((x)r,yr) Is a reference point (x)r,yr) Is the reference probability of the point to be located.
Wherein, in the absence of the positioning history data,
Figure BDA0001626753180000092
wherein M is the number of reference points.
In the presence of the location history data,
Figure BDA0001626753180000101
wherein N is the passing (x) in the positioning history datar-1,yr-1) The total number of loci of points, n being the passage (x)r-1,yr-1) The next site after the point is (x)r,yr) The number of tracks.
Step 402, determining the probability that each reference point is a point to be located according to the probability that each reference point corresponding to the detection signal strength of each AP is a point to be located.
Optionally, the specific process of determining the probability that each reference point is the point to be located according to the reference probability that each reference point is the point to be located may be: and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
Optionally, according to the reference probability that each reference point is a point to be located, the specific processing for determining the probability that each reference point is a point to be located may be specific processing: and calculating the sum of the reference probabilities corresponding to the detection signal strength of each AP aiming at each reference point, and taking the obtained calculation result as the probability that the reference point is the point to be positioned.
And step 403, determining the reference point with the maximum probability as the point to be located.
Therefore, the ray tracing assisted Bayes fingerprint positioning method provided by the embodiment of the invention can realize Bayes fingerprint matching positioning by utilizing the ray tracing method and a small amount of data acquisition statistics, and can effectively reduce the manpower consumption and time consumption required by positioning.
Based on the same technical concept, corresponding to the embodiment of the method shown in fig. 1, the embodiment of the present invention further provides a ray tracing assisted bayesian fingerprint positioning apparatus, as shown in fig. 5, the apparatus includes:
a first calculating module 501, configured to obtain, according to the transmission power of each AP in the area to be located, reference signal strength of each AP at each reference point in the area to be located through ray tracing calculation;
a second calculating module 502, configured to calculate, for each AP, a signal intensity probability distribution model of the AP according to a pre-obtained composition parameter of the gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP, and a preset probability distribution model calculation formula;
the Gaussian mixture model represents the signal intensity probability distribution of the AP at a preset acquisition point;
the positioning module 503 is configured to, when receiving a positioning request which is sent by a terminal and carries the detection signal strength of each AP at a to-be-positioned point, determine a position of the to-be-positioned point through a bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the to-be-positioned point.
Optionally, the first calculating module 501 includes:
the first calculating unit is used for determining the signal strength sent by each AP according to the transmitting power of the AP;
the second calculation unit is used for calculating a formula and corresponding barrier distribution data at each reference point according to the path loss of the wireless signal, and calculating the signal intensity loss of the signal sent by the AP at each reference point;
and the third calculating unit is used for determining the signal strength of the AP at each reference point according to the signal strength sent by the AP and the signal strength loss of the signal sent by the AP at each reference point.
Optionally, the apparatus further includes a gaussian mixture model calculation module, where the gaussian mixture model calculation module is specifically configured to:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
and aiming at each AP, determining the composition parameters of the Gaussian mixture model of the AP at a preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm.
Wherein the Gaussian mixture model represents the probability distribution of the signal strength of the AP at the preset acquisition point.
Optionally, the positioning module 503 includes:
the first positioning calculation unit is used for calculating the prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to the signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
the second positioning calculation unit is used for determining the probability that each reference point is the point to be positioned according to the reference probability that each reference point is the point to be positioned;
and the positioning point determining unit is used for determining the reference point with the maximum probability as the to-be-positioned point.
Optionally, the second positioning calculation unit is specifically configured to:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
The embodiment of the present invention further provides a server, as shown in fig. 6, including a processor 601, a communication interface 602, a memory 603, and a communication bus 604, where the processor 601, the communication interface 602, and the memory 603 complete mutual communication through the communication bus 604;
a memory 603 for storing a computer program;
the processor 601 is configured to, when executing the program stored in the memory 603, cause the node apparatus to perform the following steps, where the steps include:
calculating the reference signal intensity of each AP at each reference point in the area to be positioned according to the transmitting power of each AP in the area to be positioned and a preset ray tracing algorithm;
for each AP, calculating a signal intensity probability distribution model of the AP according to a pre-acquired composition parameter of a Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula;
wherein, the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point;
when a positioning request which is sent by a terminal and carries the detection signal strength of each AP at a point to be positioned is received, the position of the point to be positioned is determined through a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the point to be positioned.
Optionally, calculating the reference signal strength of each AP at each reference point in the area to be positioned according to the transmission power of each AP in the area to be positioned and a preset ray tracing algorithm, including:
aiming at each AP, determining the signal strength sent by the AP according to the transmitting power of the AP;
calculating the signal intensity loss of the signal sent by the AP at each reference point according to a wireless signal path loss calculation formula and barrier distribution data corresponding to each reference point;
and determining the signal strength of the AP at each reference point according to the signal strength of the AP and the signal strength loss of the AP at each reference point.
Optionally, the method further includes:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
for each AP, determining the composition parameters of a Gaussian mixture model of the AP at a preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm;
wherein the Gaussian mixture model represents the probability distribution of the signal strength of the AP at the preset acquisition point.
Optionally, determining the position of the to-be-positioned point by a bayesian algorithm according to the signal intensity probability distribution model of each AP and the detection signal intensity of each AP at the to-be-positioned point, including:
aiming at each AP, calculating the prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to the signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
determining the probability that each reference point is a point to be positioned according to the reference probability that each reference point is the point to be positioned;
and determining the reference point with the maximum probability as the point to be located.
Optionally, determining the probability that each reference point is the point to be located according to the reference probability that each reference point is the point to be located includes:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
The machine-readable storage medium may include a RAM (Random Access Memory) and may also include a NVM (Non-Volatile Memory), such as at least one disk Memory. Additionally, the machine-readable storage medium may be at least one memory device located remotely from the aforementioned processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims (10)

1. A ray tracing assisted Bayesian fingerprint positioning method is characterized by comprising the following steps:
calculating the reference signal intensity of each AP at each reference point in the area to be positioned according to the transmitting power of each AP in the area to be positioned and a preset ray tracing algorithm;
for each AP, calculating a signal intensity probability distribution model of the AP according to a pre-acquired composition parameter of a Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula, wherein the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point;
and when a positioning request which is sent by a terminal and carries the detection signal strength of each AP at the point to be positioned is received, determining the position of the point to be positioned through a Bayesian algorithm according to the signal strength probability distribution model of each AP and the detection signal strength of each AP at the point to be positioned.
2. The method according to claim 1, wherein the calculating the reference signal strength of each AP at each reference point in the area to be located according to the transmission power of each AP in the area to be located and a preset ray tracing algorithm comprises:
aiming at each AP, determining the signal strength sent by the AP according to the transmitting power of the AP;
calculating the signal intensity loss of the signal sent by the AP at each reference point according to a wireless signal path loss calculation formula and the corresponding obstacle distribution data at each reference point;
and determining the signal strength of the AP at each reference point according to the signal strength sent by the AP and the signal strength loss of the signal sent by the AP at each reference point.
3. The method of claim 1, further comprising:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
for each AP, determining the composition parameters of a Gaussian mixture model of the AP at the preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point.
4. The method according to claim 1, wherein the determining the position of the point to be positioned by a bayesian algorithm according to the signal strength probability distribution model of each AP and the detected signal strength of each AP at the point to be positioned comprises:
aiming at each AP, calculating the prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to the signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
determining the probability that each reference point is a point to be located according to the reference probability that each reference point is a point to be located;
and determining the reference point with the maximum probability as the point to be located.
5. The method according to claim 4, wherein said determining the probability that each reference point is a point to be located according to the reference probability that each reference point is a point to be located comprises:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
6. A ray tracing assisted Bayesian fingerprint locating device, characterized in that the device comprises:
the first calculation module is used for calculating through a ray tracing method according to the transmitting power of each wireless Access Point (AP) in the area to be positioned to obtain the reference signal intensity of each AP at each reference point in the area to be positioned;
the second calculation module is used for calculating a signal intensity probability distribution model of each AP according to a pre-acquired composition parameter of the Gaussian mixture model of the AP at a preset acquisition point, the reference signal intensity of the AP and a preset probability distribution model calculation formula; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point;
and the positioning module is used for determining the position of the to-be-positioned point according to the signal intensity probability distribution model of each AP and the detection signal intensity of each AP at the to-be-positioned point by a Bayesian algorithm when receiving a positioning request which is sent by a terminal and carries the detection signal intensity of each AP at the to-be-positioned point.
7. The apparatus of claim 6, wherein the first computing module comprises:
the first calculating unit is used for determining the signal strength sent by each AP according to the transmitting power of the AP;
a second calculating unit, configured to calculate, according to a wireless signal path loss calculation formula and the corresponding obstacle distribution data at each reference point, a signal strength loss of the signal sent by the AP at each reference point;
and the third calculating unit is used for determining the signal strength of the AP at each reference point according to the signal strength sent by the AP and the signal strength loss of the signal sent by the AP at each reference point.
8. The apparatus of claim 6, further comprising a Gaussian mixture model calculation module, the Gaussian mixture model calculation module being specifically configured to:
acquiring sampling data of the signal intensity of each AP at a preset acquisition point within a preset time length;
for each AP, determining the composition parameters of a Gaussian mixture model of the AP at the preset acquisition point according to the acquired sampling data of the AP signal intensity, the Gaussian mixture model formula and a preset estimation algorithm; the Gaussian mixture model represents the signal intensity probability distribution of the AP at the preset acquisition point.
9. The apparatus of claim 6, wherein the positioning module comprises:
a first positioning calculation unit, configured to calculate, for each AP, a prior probability that the signal strength of the AP at each reference point is the detection signal strength of the AP according to a signal strength probability distribution model of the AP and the detection signal strength of the AP at a point to be positioned; calculating the reference probability of each reference point as a point to be positioned according to a Bayesian rule probability formula and each calculated prior probability;
the second positioning calculation unit is used for determining the probability that each reference point is a point to be positioned according to the reference probability that each reference point is a point to be positioned;
and the positioning point determining unit is used for determining the reference point with the maximum probability as the to-be-positioned point.
10. The apparatus according to claim 9, wherein the second positioning calculation unit is specifically configured to:
and calculating the product of the reference probabilities corresponding to the detection signal strengths of the APs aiming at each reference point, wherein the obtained calculation result is the probability that the reference point is the point to be positioned.
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