CN109490826B - Ranging and position positioning method based on radio wave field intensity RSSI - Google Patents

Ranging and position positioning method based on radio wave field intensity RSSI Download PDF

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CN109490826B
CN109490826B CN201811260474.4A CN201811260474A CN109490826B CN 109490826 B CN109490826 B CN 109490826B CN 201811260474 A CN201811260474 A CN 201811260474A CN 109490826 B CN109490826 B CN 109490826B
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CN109490826A (en
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车金泽
王勇
毛钰超
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Xidian University
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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
    • G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements

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Abstract

The invention belongs to the technical field of wireless internet, internet of things and mobile communication, and discloses a distance measuring and position positioning method based on radio wave field intensity RSSI; carrying out optimization screening processing on the RSSI data value by using a Gaussian model so as to reduce the random fluctuation of the data and eliminate abnormal data samples; then, an RSSI field intensity-distance path conversion model used in the distance conversion stage and a dynamic parameter estimation method related to the RSSI field intensity-distance path conversion model are provided; and (3) carrying out real-time calibration on the model parameters by adopting a weighted parameter estimation method. Before positioning begins, parameters of a dynamic model in the current environment are estimated through mutual cooperation among anchor nodes with known positions, the distance measurement error is reduced, and the method has the advantages of being simple in calculation and easy to perform dynamic correction in the positioning process; the accuracy and the stability of wireless ranging and geometric positioning are effectively improved.

Description

Ranging and position positioning method based on radio wave field intensity RSSI
Technical Field
The invention belongs to the technical field of wireless internet, internet of things and mobile communication, and particularly relates to a distance measurement and position positioning method based on radio wave field strength RSSI.
Background
Currently, the current state of the art commonly used in the industry is such that: in recent years, with the rapid development and promotion of mobile internet and internet of things technologies, indoor wireless positioning technologies are getting more and more attention of people, and indoor positioning refers to acquiring position information of a target to be positioned by using technologies such as communication networks and wireless positioning in an indoor environment. With the appearance of a large number of various wearable devices, sensor networks and Internet of things devices, the wireless positioning service has a good hardware foundation; meanwhile, the popularization of the wireless local area network and the deployment of ubiquitous networks such as 5G provide high-speed data communication capability for indoor positioning service. Due to the special role and great commercial potential of indoor positioning technology in various fields, the development of indoor positioning technology is very rapid in recent years, wherein a ranging and positioning technology based on Received Signal Strength Indicator (RSSI) is the most widely applied indoor wireless positioning technology at present. The RSSI positioning technology has the advantages of simple algorithm, easy realization, low cost and low requirement on hardware equipment, so that the RSSI positioning technology is widely applied in the field of indoor wireless positioning at present and is studied most deeply. However, in a complex indoor environment, since radio signal propagation is easily affected by channel multipath effect, non-line-of-sight propagation, additive noise interference, dynamic changes of the indoor environment, and other factors, the RSSI wireless ranging and positioning tracking result often has a large error, so that many problems still exist in practical applications, such as: how to improve the positioning accuracy in a complex indoor environment, and improve the applicability and the expandability of a positioning system in various environments. Therefore, how to acquire accurate, stable and effective position information in view of the complexity and specificity of the propagation path of the radio waves in the indoor channel environment is a hot issue in the current research and development of the RSSI positioning technology.
In a simplified form, the basic flow of the RSSI-based wireless location system includes: firstly, collecting and correcting a received RSSI signal; further, the RSSI field intensity data is converted into distance measurement of radio wave propagation; and finally, positioning calculation of the geometric position is carried out based on a plurality of distance data. One of the major sources of RSSI wireless positioning system error is: in the stage of converting the RSSI data to the distance, the accuracy and the precision of distance measurement calculation are determined by the accuracy of the acquired RSSI value, the fluctuation range, the influence of the current indoor wireless environment on the RSSI data, the accuracy of the adopted wireless signal distance propagation model parameters and other links. In particular, the parameters in the conventional RSSI field strength-distance path conversion model are set by using fixed empirical values. However, due to changes in the system deployment environment or dynamic changes in the number and positions of the personnel in the same environment, parameters may change, such as abnormal RSSI data caused by instantaneous fluctuation of indoor channel multipath and accidental shielding of transmission paths, and if these data still participate in subsequent ranging and geometric positioning operations, large ranging and positioning errors may result. Therefore, the model parameters need to be corrected in real time during the positioning process to reduce the ranging error.
The difficulty and significance for solving the technical problems are as follows:
the indoor positioning technology based on the RSSI ranging is widely applied due to low cost and simple deployment, but the technology needs to preset RSSI field intensity-distance path conversion model parameters, namely, adopts preset empirical values. However, in an indoor channel environment in practical application, it is difficult to obtain the parameters of the indoor propagation model that randomly change with the environment in advance, and scenes such as indoor layout, change of the position of a placed object, indoor personnel number and personnel movement all change at any time, so the parameters of the relevant model need to be calibrated quickly and dynamically in time at low cost in the positioning operation process, so as to reduce the ranging error in the RSSI field strength-distance path conversion model, thereby improving the positioning accuracy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a distance measurement and position positioning method based on the RSSI (radio wave field intensity signal strength indicator).
The invention is realized in such a way that the distance measurement and position positioning method based on the radio wave field intensity RSSI utilizes a Gaussian model to carry out optimized screening processing on the data value of the RSSI; giving an RSSI field intensity-distance path conversion model used in a distance conversion stage; before positioning begins, parameters of the dynamic model in the current environment are estimated through mutual cooperation among anchor nodes with known positions.
Further, the ranging and position locating method based on the radio wave field strength RSSI specifically includes:
step one, initializing an anchor node and a Tag node: firstly, periodically broadcasting a request positioning signal by a Tag node of a terminal to be positioned, and all L indoor positioning Anchor nodes Anchor 1 ,Anchor 2 …,Anchor l ,…Anchor L Starting to receive a broadcast signal transmitted by a Tag node of a terminal to be positioned;
step two, RSSI sample data screening pretreatment: after L positioning anchor nodes receive broadcast signals transmitted by a terminal node Tag to be positioned, the L positioning anchor nodes respectively extract corresponding RSSI values from the broadcast signals;
step three, selecting the beacon anchor node which is the nearest to the Tag node to be positioned: the Tag node receives t 0 RSSI value returned by L anchor nodes at the momentAnd store it into an array vector
Figure BDA0001843777430000031
The method comprises the following steps:
Figure BDA0001843777430000032
log re-array
Figure BDA0001843777430000033
The RSSI samples in (1) are ordered in turn according to the magnitude of the values:
Figure BDA0001843777430000034
wherein
Figure BDA0001843777430000035
Is shown at t 0 The first sample after the time instant is reordered according to size. Accordingly, 4 RSSI values with the maximum value are selected
Figure BDA0001843777430000036
Taking the corresponding anchor node as the nearest beacon anchor node of the currently to-be-positioned Tag node: { Anchor (1) ,Anchor (2) ,Anchor (3) ,Anchor (4) }; still further, a confidence weighting factor k for the model parameters is calculated l (l＝1,2,3,4)；
Figure BDA0001843777430000037
Wherein κ l The reliability weighting factor of the model parameter called electric wave field intensity-distance path conversion is
Figure BDA0001843777430000038
It can anchor nodes according to 4 beacons
Figure BDA0001843777430000039
To determine the size range between:
Figure BDA00018437774300000310
the larger the weight factor k, the higher the confidence of the parameter l The larger the occupied value should be; the smaller the opposite is;
step four, obtaining RSSI data between beacon anchor nodes: periodically transmitting broadcast signals containing self identity IDs by the selected 4 beacon anchor nodes, receiving and recording RSSI values and ID numbers among the beacon anchor nodes, and performing screening optimization processing on the RSSI as the second step;
step five, establishing a field intensity RSSI-distance path conversion model: suppose a beacon Anchor node Anchor (1) Transmitting signals, noting the beacon Anchor node Anchor (2) And beacon Anchor node Anchor (3) The received RSSI is respectively RSSI after being screened and optimized 12 And RSSI 13 Establishing a radio wave field intensity-propagation distance path conversion model:
Figure BDA0001843777430000041
wherein, pref 1 Is an Anchor node Anchor (1) Corresponding distance reference power parameter, η 1 Is its path loss exponent parameter; dist 12 Anchor node Anchor known at the time of deployment for indoor positioning system (1) And Anchor node Anchor (2) Distance between, dist 13 The same is indicated;
solving the equation set to obtain the parameter Pref 1 And η 1 A set of solutions of:
Figure BDA0001843777430000042
separate determination of the beacon Anchor (2) And Anchor (3) Parameter estimation Pref with respect to this region 2 、η 2 And Pref 3 、η 3 ：
Figure BDA0001843777430000043
Figure BDA0001843777430000051
Figure BDA0001843777430000052
Wherein, dist ij ＝dist ji (i,j＝1,2,3,4,i≠j)；
Step six, estimating parameters of a field strength RSSI-distance path conversion model: each beacon anchor node sends its own ID information and parameter Pref l 、η l The estimated value of (l =1,2,3,4) is sent to a Tag node to be positioned, and the Tag node calculates the final field intensity-distance path conversion model parameter estimated value of the area according to the following formula:
Figure BDA0001843777430000053
wherein, pref Tag For the estimated distance reference power parameter, eta, of the Tag node of the terminal to be positioned Tag Then its path loss exponent parameter, κ l (l =1,2,3,4) is the reliability weighting factor of the radio field intensity-distance path conversion model parameter obtained in the third step;
step seven, obtaining a conversion formula of the RSSI and the distance measurement of the Tag node: obtaining the Tag node to be positioned at t 0 The conversion formula from the signal field strength RSSI measurement to the distance measurement at the moment is as follows:
Figure BDA0001843777430000054
wherein the RSSI Tag The RSSI field strength data to be converted for the positioning node, and the Distance is the Distance measurement obtained by conversion and used for the subsequent RSSI field strength data to be converted for the positioning nodeAnd (5) geometric analysis operation for positioning the geographical position of the Tag node.
Further, the step of preprocessing the received RSSI sample data by filtering specifically includes:
the first step is as follows: anchor node Anchor will be located l At t 0 The RSSI value collected at the moment is stored in the original RSSI received data vector
Figure BDA0001843777430000061
Figure BDA0001843777430000062
Wherein N is t 0 Anchor node Anchor of the moment l The maximum value of the number of the obtained RSSI samples is marked by L (L is more than 0 and less than or equal to L) to indicate that the RSSI samples belong to the ith anchor node;
the second step is that: separately calculating the original received data vectors
Figure BDA0001843777430000063
Sample mean and sample variance in (1):
Figure BDA0001843777430000064
RSSI sample variance:
Figure BDA0001843777430000065
and (3) fitting an RSSI probability density function pdf based on Gaussian statistical distribution:
Figure BDA0001843777430000066
the third step: sample mean value according to RSSI
Figure BDA0001843777430000067
Sum sample variance
Figure BDA0001843777430000068
Determining the upper and lower limits of RSSI sample value to be optimized and screened, and distributing function value F by Gaussian statistics Pdf Is equal to 1 and
Figure BDA0001843777430000069
respectively serving as upper and lower limit critical points for screening RSSI sample data:
Figure BDA00018437774300000610
wherein the content of the first and second substances,
Figure BDA00018437774300000611
referred to as RSSI screening probability threshold; calculating the lower threshold value for screening RSSI sample data
Figure BDA00018437774300000612
And an upper threshold
Figure BDA00018437774300000613
The fourth step: the RSSI sample data optimization screening rule is as follows: retaining only the original data vector RSSI Original In the middle of
Figure BDA00018437774300000614
RSSI sample data within the range is stored in the filtered RSSI vector:
Figure BDA0001843777430000071
wherein M is the original data vector RSSI Original The number of RSSI samples in the set that meet the screening rules,
Figure BDA0001843777430000072
the subscript is the reordered sequence number of the RSSI data samples after screening treatment;
the fifth step: for screening RSSI vector RSSI Selected The average value of the sample data in (1) is obtained and is used as a positioning Anchor node Anchor l At t 0 And the RSSI optimized value between the terminal Tag node to be positioned and the terminal obtained at the moment is as follows:
Figure BDA0001843777430000073
and a sixth step: l positioning Anchor nodes Anchor l (l is more than or equal to 4) to be optimized
Figure BDA0001843777430000074
The value is returned to the terminal node Tag to be positioned so as to participate in the subsequent conversion calculation from the RSSI value to the propagation distance.
Another objective of the present invention is to provide an RSSI wireless positioning system using the method for measuring distance and positioning based on the RSSI of the radio field intensity.
Another objective of the present invention is to provide a real-time correction system for indoor wireless environment parameters using the distance measurement and position location method based on the RSSI.
In summary, the advantages and positive effects of the invention are: performing optimized screening processing on the RSSI data value by using a Gaussian model to reduce the random fluctuation of the data and eliminate abnormal data samples; and then, an RSSI field intensity-distance path conversion model used in the distance conversion stage and a dynamic parameter estimation method related to the RSSI field intensity-distance path conversion model are provided, and the method adopts a weighting parameter estimation method to carry out real-time calibration on model parameters. The main idea is as follows: before positioning begins, parameters of a dynamic model under the current environment are estimated through mutual cooperation among anchor nodes with known positions, so that the ranging error is reduced, and the method has the advantages of being simple in calculation and easy to perform dynamic correction in the positioning process. The method can be widely applied to the stages of rapid deployment of the RSSI wireless positioning system and real-time correction of indoor wireless environment parameters, so that the accuracy and stability of wireless ranging and geometric positioning are effectively improved. The method of the invention fully utilizes the RSSI information of all anchor nodes which are communicated with the terminal node to be positioned, can simultaneously estimate the parameters of the current channel model, and compared with the traditional RSSI data averaging method, the method can effectively improve the estimation precision of the distance measurement parameters and improve the adaptability to various indoor environments; compared with a complex maximum likelihood parameter estimation method, the method avoids high-complexity iterative computation required by the method, and the ranging precision is irrelevant to the initial positioning position. The invention particularly relates to a ranging and position positioning method based on radio wave field intensity RSSI, which can be used in the fields of wireless ranging, rapid deployment of an indoor wireless positioning network, indoor positioning and position detection, intelligent robot motion control and the like.
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Fig. 1 is a flowchart of a ranging and position locating method based on RSSI according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a basic architecture of an RSSI indoor wireless positioning system according to an embodiment of the present invention.
Fig. 3 is a flowchart of an implementation of a distance measuring and position locating method based on RSSI according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of parameter estimation of a field strength-distance path transformation model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method aims at the problem that the existing RSSI field intensity-distance path conversion model has large distance measurement errors; before positioning begins, the dynamic model parameters under the current environment are estimated through mutual cooperation among anchor nodes with known positions, so that the ranging error is reduced, and the method has the advantages of simple calculation and easiness in dynamic correction in the positioning process.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, a ranging and position locating method based on RSSI according to an embodiment of the present invention includes the following steps:
s101: analyzing and correcting the acquired RSSI original data, and performing optimized screening processing on the RSSI data value by using a Gaussian model so as to reduce the random fluctuation of the data and eliminate abnormal data samples;
s102: and then, an RSSI field intensity-distance path conversion model used in the distance conversion stage and a dynamic parameter estimation method related to the RSSI field intensity-distance path conversion model are provided.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
The distance measurement and position positioning method based on the RSSI of the radio wave field provided by the embodiment of the invention firstly analyzes and corrects the acquired RSSI original data, and utilizes a Gaussian model to carry out optimized screening processing on the data value of the RSSI so as to reduce the random fluctuation of the data and eliminate abnormal data samples; then, an RSSI field intensity-distance path conversion model used in the distance conversion stage and a dynamic parameter estimation method related to the RSSI field intensity-distance path conversion model are provided; the model parameters are calibrated in real time by adopting a weighted parameter estimation method, and the main idea is as follows: before positioning begins, parameters of a dynamic model under the current environment are estimated through mutual cooperation among anchor nodes with known positions, so that the ranging error is reduced, and the method has the advantages of being simple in calculation and easy to perform dynamic correction in the positioning process. The method can be widely used in the stages of rapid deployment of the RSSI wireless positioning system and real-time correction of indoor wireless environment parameters, thereby effectively improving the accuracy and stability of wireless ranging and geometric positioning.
The basic architecture of the positioning system is shown in FIG. 2, and it is assumed that there are L (L is more than or equal to 4) fixed positioning Anchor nodes with known positions in the indoor environment where the Tag node of the terminal to be positioned is located 1 ,Anchor 2 …,Anchor l ,…Anchor L The geometric coordinates of the indoor site are known as (x) 1 ,y 1 ),(x 2 ,y 2 ),…(x l ,y l )…(x L ,y L ) (ii) a Enabling timing of phases between anchor nodes of the systemAnd (4) communicating with each other. Without loss of generality, because the terminal node Tag to be positioned and the anchor nodes closest to the terminal node Tag to be positioned are relatively located in a smaller geographical range, the characteristics of the wireless signal space propagation environment between the anchor nodes can be approximately considered to be the same as those between the anchor nodes and the terminal node Tag to be positioned. The method aims to utilize the position information estimation of surrounding anchor nodes to optimize and screen the signal field intensity RSSI data of the Tag node of the terminal to be positioned in the range close to the surrounding electromagnetic environment, and further accurately and stably estimates the parameters related to the RSSI-to-distance conversion model in real time.
As shown in fig. 3, the specific implementation steps of the ranging and position locating method based on the radio wave field strength RSSI provided by the embodiment of the present invention are as follows:
step 1, initializing an anchor node and a Tag node: firstly, periodically broadcasting a request positioning signal by a Tag node of a terminal to be positioned, and all L indoor positioning Anchor nodes Anchor 1 ,Anchor 2 …,Anchor l ,…Anchor L Starting to receive a broadcast signal transmitted by a Tag node of a terminal to be positioned;
step 2, RSSI sample data screening pretreatment: after L positioning anchor nodes receive the broadcast signals transmitted by the Tag of the terminal node to be positioned, the corresponding RSSI values are respectively extracted from the broadcast signals. In order to eliminate the influence caused by RSSI instantaneous fluctuation due to indoor environment change and interference, the method performs optimization screening processing on original RSSI sample data based on a Gaussian probability distribution statistical model, and aims to eliminate RSSI singular value sample data with larger interference so as to ensure the accuracy of subsequent field intensity-distance conversion. Below to locate the Anchor node Anchor l For example, the filtering pre-processing procedure for received RSSI sample data is described.
The first step is as follows: anchor node Anchor will be located l At t 0 The RSSI value collected at the moment is stored in the original RSSI received data vector
Figure BDA0001843777430000101
Figure BDA0001843777430000102
Wherein N is t 0 Anchor node Anchor of the moment l The maximum number of RSSI samples obtained, superscript L (0 < L ≦ L), indicates belonging to the ith anchor node.
The second step is that: separately calculating the original received data vectors
Figure BDA0001843777430000103
Sample mean and sample variance in (1):
Figure BDA0001843777430000104
RSSI sample variance:
Figure BDA0001843777430000105
based on the two data, an RSSI probability density function pdf based on Gaussian statistical distribution is fitted:
Figure BDA0001843777430000106
the third step: mean value of samples according to RSSI
Figure BDA0001843777430000107
Sum sample variance
Figure BDA0001843777430000108
Determining the upper and lower limits of RSSI sample value to be optimized and screened, and distributing function value F by Gaussian statistics Pdf Is equal to 1 and
Figure BDA0001843777430000109
respectively serving as upper and lower limit critical points for screening RSSI sample data:
Figure BDA0001843777430000111
wherein the content of the first and second substances,
Figure BDA0001843777430000112
referred to as RSSI screening probability threshold. The lower threshold value for RSSI sample data screening can be calculated according to the formula
Figure BDA0001843777430000113
And an upper threshold
Figure BDA0001843777430000114
The fourth step: the RSSI sample data optimization screening rule is as follows: retaining only the original data vector RSSI Original In the middle of
Figure BDA0001843777430000115
RSSI sample data within the range is stored in the filtered RSSI vector:
Figure BDA0001843777430000116
wherein M is the original data vector RSSI Original The number of RSSI samples that meet the screening rules,
Figure BDA0001843777430000117
the subscript is the reordered sequence number of the RSSI data samples after the screening process.
The fifth step: for screening RSSI vector RSSI Selected The average value of the sample data in (1) is obtained and is used as a positioning Anchor node Anchor l At t 0 And the RSSI optimized value between the terminal Tag node to be positioned and the terminal obtained at the moment is as follows:
Figure BDA0001843777430000118
and a sixth step: l positioning Anchor nodes Anchor l (l is more than or equal to 4) to be optimized
Figure BDA0001843777430000119
The value is returned to the terminal node Tag to be positioned so as to participate in the subsequent conversion calculation from the RSSI value to the propagation distance.
Step 3, selecting the beacon anchor node which is the nearest to the Tag node to be positioned: the Tag node receives t 0 The RSSI values returned by the L anchor nodes at the moment are firstly stored into an array vector
Figure BDA00018437774300001110
The method comprises the following steps:
Figure BDA00018437774300001111
log re-array
Figure BDA00018437774300001112
The RSSI samples in (1) are ordered in turn according to the magnitude of the values:
Figure BDA00018437774300001113
wherein
Figure BDA00018437774300001114
Is shown at t 0 The first sample after the time instant is reordered according to size. Accordingly, 4 RSSI values with the maximum value are selected
Figure BDA00018437774300001115
Taking the corresponding anchor node as the nearest beacon anchor node of the Tag node to be positioned currently: { Anchor (1) ,Anchor (2) ,Anchor (3) ,Anchor (4) }. Still further, a confidence weighting factor k for the model parameters is calculated l (l =1,2,3,4), let
Figure BDA0001843777430000121
Wherein κ l The reliability weighting factor of the model parameter called electric wave field intensity-distance path conversion is
Figure BDA0001843777430000122
It can anchor nodes according to 4 beacons
Figure BDA0001843777430000123
To determine the size range between:
Figure BDA0001843777430000124
the larger the parameter, the higher the confidence of the parameter, i.e. its corresponding weighting factor k l The larger the occupied value should be; the smaller should be the reverse.
Step 4, obtaining RSSI data between beacon anchor nodes: the selected 4 beacon anchor nodes periodically transmit broadcast signals containing self identity IDs, and simultaneously, the respective beacon anchor nodes receive and record RSSI values and ID numbers between themselves, and perform the screening optimization processing as shown in step 2 on the RSSI, as shown in fig. 4.
Step 5, establishing a field strength RSSI-distance path conversion model: suppose a beacon Anchor node Anchor (1) Transmitting signals, noting the beacon Anchor node Anchor (2) And beacon Anchor node Anchor (3) The received RSSI is respectively RSSI after being screened and optimized 12 And RSSI 13 Establishing a radio wave field intensity-propagation distance path conversion model:
Figure BDA0001843777430000125
wherein, pref 1 Is an Anchor node Anchor (1) Corresponding distance reference power parameter, η 1 Is its path loss exponent parameter; dist 12 Anchor node Anchor known at the time of deployment for indoor positioning system (1) And Anchor node Anchor (2) Distance between, dist 13 The same applies to the representation.
The parameter Pref can be obtained by solving the above equation set 1 And η 1 A set of solutions of:
Figure BDA0001843777430000131
similarly, the beacon Anchor can be obtained separately (2) And Anchor (3) Parameter estimation Pref with respect to this region 2 、η 2 And Pref 3 、η 3 ：
Figure BDA0001843777430000132
Figure BDA0001843777430000133
Figure BDA0001843777430000134
Wherein, dist ij ＝dist ji (i,j＝1,2,3,4,i≠j)。
Step 6, estimating parameters of a field strength RSSI-distance path conversion model: each beacon anchor node sends its own ID information and parameter Pref l 、η l And (l =1,2,3,4) sending the estimated value to a Tag node to be positioned, and calculating the final field intensity-distance path conversion model parameter estimated value of the region by the Tag node according to the following formula:
Figure BDA0001843777430000141
wherein, pref Tag For the estimated distance reference power parameter, eta, of the Tag node of the terminal to be positioned Tag Then its path loss exponent parameter, κ l (l =1,2,3,4) is the reliability weighting factor of the radio field intensity-distance path conversion model parameter obtained in step 3.
Step 7, obtaining a conversion formula of the RSSI and the distance measurement of the Tag node: obtaining a Tag node to be positioned at t 0 The time is measured by the signal field strength RSSIConversion formula between the amounts of separation:
Figure BDA0001843777430000142
wherein the RSSI Tag The Distance is the Distance measurement obtained by conversion for the RSSI field intensity data to be converted of the positioning node, and can be used for the subsequent geometric analysis operation for the geographical position of the Tag node to be positioned.
The steps can be repeatedly executed according to the updating requirement of the system, and the conversion model parameters of the Tag node to be positioned are dynamically updated in real time.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1. A distance measurement and position positioning method based on radio wave field intensity RSSI is characterized in that the distance measurement and position positioning method based on radio wave field intensity RSSI utilizes a Gaussian model to carry out optimized screening processing on RSSI data values; giving an RSSI field intensity-distance path conversion model used in a distance conversion stage; before positioning begins, estimating dynamic model parameters under the current environment through mutual cooperation among anchor nodes with known positions;
the ranging and position positioning method based on the radio wave field strength RSSI specifically comprises the following steps:
step one, initializing an anchor node and a Tag node: firstly, periodically broadcasting a request positioning signal by a Tag node of a terminal to be positioned, and all L indoor positioning Anchor nodes Anchor 1 ,Anchor 2 …,Anchor l ,…Anchor L Starting to receive a broadcast signal transmitted by a Tag node of a terminal to be positioned;
step two, RSSI sample data screening pretreatment: after L positioning anchor nodes receive broadcast signals transmitted by a terminal node Tag to be positioned, the L positioning anchor nodes respectively extract corresponding RSSI values from the broadcast signals;
step three, selecting the beacon anchor node which is the nearest to the Tag node to be positioned: the Tag node receives t 0 Firstly, the RSSI values returned by L anchor nodes at the moment are stored into an array vector
Figure FDA0003911036050000011
The method comprises the following steps:
Figure FDA0003911036050000012
log re-array
Figure FDA0003911036050000013
The RSSI samples in (1) are ordered in turn according to the magnitude of the values:
Figure FDA0003911036050000014
wherein
Figure FDA0003911036050000015
Is shown at t 0 The first sample after the time is rearranged according to the size; accordingly, 4 RSSI values with the maximum value are selected
Figure FDA0003911036050000016
Taking the corresponding anchor node as the nearest beacon anchor node of the Tag node to be positioned currently:
Figure FDA0003911036050000019
still further, a confidence weighting factor k for the model parameters is calculated l ，l＝1，2，3，4；
Figure FDA0003911036050000017
Wherein κ l Called electric wave field strength-distanceConfidence weighting factors of the path conversion model parameters are
Figure FDA0003911036050000018
It can be based on 4 beacon anchor nodes
Figure FDA0003911036050000021
To determine the size range between:
Figure FDA0003911036050000022
the larger the weight factor k, the higher the confidence of the parameter l The larger the occupied value should be; the smaller the opposite is;
step four, obtaining RSSI data between beacon anchor nodes: periodically transmitting broadcast signals containing self identity IDs by the selected 4 beacon anchor nodes, receiving and recording RSSI values and ID numbers among the beacon anchor nodes, and performing screening optimization processing on the RSSI as the second step;
step five, establishing a field intensity RSSI-distance path conversion model: suppose a beacon Anchor node Anchor (1) Transmitting signals, noting the beacon Anchor node Anchor (2) And beacon Anchor node Anchor (3) The received RSSI is respectively RSSI after being screened and optimized 12 And RSSI 13 Establishing a radio wave field intensity-propagation distance path conversion model:
Figure FDA0003911036050000023
wherein, pref 1 Is an Anchor node Anchor (1) Corresponding distance reference power parameter, η 1 Is its path loss exponent parameter; dist 12 Anchor node Anchor known at the time of deployment for indoor positioning system (1) And Anchor node Anchor (2) Distance between, dist 13 The same is shown;
solving the equation set to obtain the parameter Pref 1 And η 1 A set of solutions of:
Figure FDA0003911036050000024
separate determination of the beacon Anchor (2) And Anchor (3) Is estimated by the parameters Pref 2 、η 2 And Pref 3 、η 3 ：
Figure FDA0003911036050000031
Figure FDA0003911036050000032
Figure FDA0003911036050000033
Wherein, dist ij ＝dist ji ，i，j＝1，2，3，4，i≠j；
Step six, estimating parameters of a field intensity RSSI-distance path conversion model: each beacon anchor node sends its own ID information and parameter Pref l 、η l And the estimated value of l =1,2,3,4 is sent to a Tag node to be positioned, and the Tag node calculates the final field intensity-distance path conversion model parameter estimated value of the area according to the following formula:
Figure FDA0003911036050000034
wherein, pref Tag For the estimated distance reference power parameter, eta, of the Tag node of the terminal to be positioned Tag Then its path loss exponent parameter, κ l L =1,2,3,4 is the credibility weighting factor of the electric wave field intensity-distance path conversion model parameter obtained in the third step;
step seven, obtaining a conversion formula of the RSSI and the distance measurement of the Tag node: obtaining a Tag node to be positioned at t 0 The conversion formula from signal field strength RSSI measurement to distance measurement at the moment is as follows:
Figure FDA0003911036050000041
wherein the RSSI Tag And for the RSSI field intensity data to be converted of the positioning node, the Distance is the Distance measurement obtained by conversion and is used for the subsequent geometric analysis operation of the geographical position of the Tag node to be positioned.
2. The method of claim 1, wherein the step of preprocessing the filtering of the received RSSI sample data comprises:
the first step is as follows: anchor node Anchor will be located l At t 0 The RSSI value collected at the moment is stored in the original RSSI received data vector
Figure FDA0003911036050000042
Figure FDA0003911036050000043
Wherein N is t 0 Anchor node Anchor of the moment l The maximum value of the number of the obtained RSSI samples is marked with L, wherein L is more than 0 and less than or equal to L;
the second step is that: separately calculating the original received data vectors
Figure FDA0003911036050000044
Sample mean and sample variance in (1):
mean RSSI sample values:
Figure FDA0003911036050000045
RSSI sample variance:
Figure FDA0003911036050000046
and (3) fitting an RSSI probability density function pdf based on Gaussian statistical distribution:
Figure FDA0003911036050000047
the third step: mean value of samples according to RSSI
Figure FDA0003911036050000048
Sum sample variance
Figure FDA00039110360500000411
Determining the upper and lower limits of RSSI sample value to be optimized and screened, and distributing function value F by Gaussian statistics Pdf Is equal to 1 and
Figure FDA0003911036050000049
respectively serving as upper and lower limit critical points for screening RSSI sample data:
Figure FDA00039110360500000410
wherein the content of the first and second substances,
Figure FDA0003911036050000051
referred to as RSSI screening probability threshold; calculating the lower threshold value for screening RSSI sample data
Figure FDA0003911036050000052
And an upper threshold
Figure FDA0003911036050000053
The fourth step: the RSSI sample data optimization screening rule is as follows: retaining only the original data vector RSSI Original In the middle of
Figure FDA0003911036050000054
RSSI sample data within the range is stored in the filtered RSSI vector:
Figure FDA0003911036050000055
wherein M is the original data vector RSSI Original The number of RSSI samples in the set that meet the screening rules,
Figure FDA0003911036050000056
the subscript is the reordered sequence number of the RSSI data samples after screening treatment;
the fifth step: for screening RSSI vector RSSI Selected The average value of the sample data in (1) is obtained and is used as a positioning Anchor node Anchor l At t 0 And the RSSI optimized value between the terminal Tag node to be positioned and the terminal obtained at the moment is as follows:
Figure FDA0003911036050000057
and a sixth step: l positioning Anchor nodes Anchor l L is more than or equal to 4
Figure FDA0003911036050000058
The value is returned to the terminal node Tag to be positioned so as to participate in the subsequent conversion calculation from the RSSI value to the propagation distance.
3. An RSSI wireless positioning system applying the distance measuring and position positioning method based on the radio wave field intensity RSSI according to any one of the claims 1-2.
4. A real-time correction system for indoor wireless environment parameters by applying the distance measurement and position location method based on the radio field strength RSSI according to any one of claims 1-2.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104093205A (en) * 2014-07-14 2014-10-08 国家电网公司 Method for deploying anchor nodes of wireless positioning system based on received signal strength indication
WO2015172458A1 (en) * 2014-05-14 2015-11-19 中国科学院沈阳自动化研究所 Rssi positioning method based on frequency-hopping spread spectrum technology
CN108535687A (en) * 2018-03-20 2018-09-14 西安电子科技大学 Indoor wireless positioning method based on the fusion of TOF and RSSI information

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015172458A1 (en) * 2014-05-14 2015-11-19 中国科学院沈阳自动化研究所 Rssi positioning method based on frequency-hopping spread spectrum technology
CN104093205A (en) * 2014-07-14 2014-10-08 国家电网公司 Method for deploying anchor nodes of wireless positioning system based on received signal strength indication
CN108535687A (en) * 2018-03-20 2018-09-14 西安电子科技大学 Indoor wireless positioning method based on the fusion of TOF and RSSI information

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于路径损耗模型参数实时估计的无线定位方法;李瑶怡等;《传感技术学报》;20100920(第09期);全文 *

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