CN113691934A - UWB indoor positioning method based on LOS credibility recognition - Google Patents

UWB indoor positioning method based on LOS credibility recognition Download PDF

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Publication number
CN113691934A
CN113691934A CN202110965796.4A CN202110965796A CN113691934A CN 113691934 A CN113691934 A CN 113691934A CN 202110965796 A CN202110965796 A CN 202110965796A CN 113691934 A CN113691934 A CN 113691934A
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base station
algorithm
distance
los
positioning
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左韬
鲁子玉
闵华松
林云汉
王少威
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Wuhan University of Science and Engineering WUSE
Wuhan University of Science and Technology WHUST
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • G01S5/0009—Transmission of position information to remote stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings

Abstract

The invention discloses a UWB indoor positioning method based on LOS credibility identification, which comprises the following steps: the method comprises the steps of obtaining initial distance information from a UWB tag to a positioning base station by using ToA and AoA algorithms respectively, conducting LOS credibility sequencing based on a Wylie recognition algorithm, conducting distance reconstruction after screening the base stations, further constructing a proper target equation set, using an initial position obtained by the newly selected base station through the ToA algorithm as an iteration initial value of a Talor-BFGS combined algorithm, conducting iteration for multiple times to gradually approach to real coordinate information of the UWB tag until a preset error threshold value is met, and outputting a final result. The positioning method can effectively weaken the influence of NLOS error on positioning precision in indoor positioning, and can provide a more reliable target equation set and a positioning initial value for the Talor-BFGS combined algorithm in the iterative process, so that the iterative algorithm can output position information with higher precision.

Description

UWB indoor positioning method based on LOS credibility recognition
Technical Field
The invention is suitable for the field Of Ultra Wide Band (UWB) indoor positioning, and particularly relates to LOS (line Of sight) reliability (possibility Of being determined as LOS) determination based on a Wylie recognition algorithm and a Talor-BFGS combined positioning algorithm, and research Of a UWB positioning tag high-precision position method is obtained by adopting chain iteration.
Background
The research on indoor positioning is caused by the fact that due to the fact that buildings are more and more complex, the position requirements of objects, people and the like in the indoor environment are increased.
The Ultra Wide Band (UWB) technology is a short-distance carrier-free wireless transmission technology, has extremely wide bandwidth and strong multi-path resolution capability, is very suitable for high-precision positioning in a short-distance multi-path environment, and has the advantages that many wireless transmission technologies are difficult to achieve in the field of indoor positioning.
The positioning algorithm based on Time of Arrival (TOA) calculates the distance from a tag of a base station to the base station according to the measured Time of a received signal between the tag and the base transmission Time, thereby calculating the position of the tag relative to the base station.
And the Wylie identification algorithm is used for judging whether the non-line-of-sight error exists in the distance or time measurement data by combining the measured distance or time value between each positioning base station and the label with the measurement noise standard deviation and the residual error analysis rank test.
The Talor series expansion algorithm is an iterative algorithm, which expands the nonlinear hyperbolic equation to be solved at the position of a given initial value by the preset initial value, and gradually approaches the real position of the positioning label after multiple iterations until the estimation error is smaller than the set error value.
A quasi-Newton iterative algorithm (BFGS method) is based on a Newton iterative algorithm, an approximate Hessian matrix without a second partial derivative is constructed under the quasi-Newton condition, the target function is subjected to Talor expansion through a Talor formula, and an obtained estimated value is continuously approximated to a true value solved by the target function through multiple iterations.
Disclosure of Invention
Aiming at the problems that the distance measurement in the ToA algorithm can generate large errors due to NLOS errors, so that the indoor positioning accuracy of UWB is greatly influenced, and the final position can also be influenced by the initial value selection of the Talor series expansion algorithm and the BFGS quasi-Newton iteration algorithm, the invention provides a UWB indoor positioning method based on LOS credibility identification.
The specific invention content is as follows:
1. a UWB indoor positioning method based on LOS credibility identification comprises the following steps:
step 1) resolving the distance from the UWB positioning tag to the base station by using a ToA algorithm and an AoA algorithm respectively, wherein the step 1) comprises the following steps:
step 1.1) obtaining the distance d between the positioning label and the base station i by using a ToA algorithmiT；
Step 1.2) using an AoA algorithm to calculate an included angle between the tag and the base station and calculating the distance d from the UWB tag to the base station iiA；
According to the sine theorem:
Figure BDA0003223836110000021
then
Figure BDA0003223836110000031
Wherein alpha isiAngle information obtained for AoA,/i,i+1Is the distance of base station i to i +1, and therefore
Figure BDA0003223836110000032
The distance d can be obtainediA；
Step 2) based on LOS reliability of Wylie recognition algorithm, the possibility of being judged as a line-of-sight environment can be obtained, and the step 2) comprises the following steps:
step 2.1) respectively calculating the variance of the positioning information from each base station channel according to the Wylie identification algorithm
Figure BDA0003223836110000033
Wherein
Figure BDA0003223836110000034
For the variance, s, of channel positioning information from base station ii(t) is the distance measurement from time t to the ith base station, and ri(t) is the measurement after polynomial smoothing;
step 2.2) minimum variance
Figure BDA0003223836110000035
The corresponding base station is set as base station m with a variance of
Figure BDA0003223836110000036
Selecting four base stations with minimum variance as BSm，BS1，BS2，BS3Corresponding variance is
Figure BDA0003223836110000037
Figure BDA0003223836110000038
The relationship of size is
Figure BDA0003223836110000039
Base station BSm，BS1，BS2，BS3Has LOS reliability of BSm>BS1>BS2>BS3That is, the signal of the base station m is the highest reliability in the LOS environment;
step 3) reconstructing the distance information obtained in step 1), wherein step 3) comprises the following steps:
step 3.1) constructing new distance information by using ToA ranging information and AoA derived ranging information
di＝λi1diT+λi2diA
Wherein λi1+λi2＝1；
Step 3.2) according to the variance value of the positioning information in the channels m,1,2 and 3 of the base station obtained in the step 2), order
Figure BDA00032238361100000310
Wherein i ═ m,1,2, 3;
step 3.3) when
Figure BDA0003223836110000041
When each base station is affected by NLOS to approximate, then order
Figure BDA0003223836110000042
When in use
Figure BDA0003223836110000043
When the influence degree difference of each base station by NLOS is obvious, the order is
Figure BDA0003223836110000044
When in use
Figure BDA0003223836110000045
When in use, will
Figure BDA0003223836110000046
Is scaled toInterval(s)
Figure BDA0003223836110000047
At the same time
Figure BDA0003223836110000048
Scaling to equal degree, and returning to the step 3.3) for assignment;
step 4) according to the distance information obtained in the step, using a Talor-BFGS joint algorithm to carry out final position calculation, wherein the step 4) comprises the following steps:
step 4.1) iteration is carried out by using a Talor series expansion algorithm, wherein the TDOA equation set is obtained by the base station and distance reconstruction value obtained in the step 2) and the step 3), and an iteration initial value of the Talor series expansion algorithm is obtained by resolving through a ToA algorithm;
and 4.2) adopting the positioning information obtained by the Talor series expansion algorithm as an iteration initial value of a BFGS quasi-Newton iteration algorithm to carry out BFGS quasi-Newton iteration and output a final position.
2. According to the UWB indoor positioning method based on LOS credibility identification, in the LOS credibility judgment based on Wylie identification algorithm in the step 2), base station selection is carried out according to the magnitude sequence of the measurement noise variance in the Wylie identification algorithm, and judgment on NLOS is not carried out.
3. According to the UWB indoor positioning method based on LOS credibility identification, wherein the distance reconstruction in the step 3) is based on ToA and AoA ranging data, wherein the AoA ranging data is calculated according to the measuring angle and the distance between the base stations.
The invention has the following advantages and beneficial effects:
base stations are selected in a sequencing mode by using LOS (degree of being determined as LOS) credibility based on a Wylie recognition algorithm, and distance data obtained by ToA and AoA are reconstructed after the base stations are selected, so that the accuracy of a distance construction target equation set is optimized;
in the chain type iteration process, because the initial value influences the iteration times and the solving precision of the algorithm to a great extent, the selection of either the target equation set or the initial value is very beneficial to iteration, and therefore, the iteration algorithm can output more high-precision position information.
Drawings
FIG. 1 is a general flow chart of the positioning method of the present invention;
FIG. 2 is a flowchart of LOS confidence determination and distance reconstruction algorithm based on Wylie recognition algorithm.
Detailed Description
The advantages and objects of the present invention will be described in further detail with reference to the accompanying drawings and examples, it being understood that the description is illustrative of the invention and is not to be construed as limiting the invention.
Supposing that n (n is more than m) base stations are arranged, measuring the distance between a positioning label and the positioning label, respectively using ToA and AoA algorithms to obtain basic distance information, after LOS credibility judgment, performing distance reconstruction to obtain credible distance information, thereby constructing a proper target equation set, selecting an initial positioning value beneficial to iteration, then performing Talor-BFGS combined algorithm iteration solution to calculate a final position, wherein the attached figure 1 is a general flow chart of a positioning method, and the attached figure 2 is a LOS credibility judgment and distance reconstruction algorithm flow chart based on Wylie recognition algorithm, and comprises the following steps:
step 1) resolving the distance from the UWB positioning tag to the base station by using a ToA algorithm and an AoA algorithm respectively, wherein the step 1) comprises the following steps:
step 1.1) obtaining the distance d from the UWB tag to the base station i by using a ToA algorithmiT；
Step 1.2) using an AoA algorithm to calculate an included angle between the tag and the base station and calculating the distance d from the UWB tag to the base station iiA；
According to the sine theorem:
Figure BDA0003223836110000061
then
Figure BDA0003223836110000062
Wherein alpha isiAngle information obtained for AoA,/i,i+1Is the distance of base station i to i +1, and therefore
Figure BDA0003223836110000063
The distance d can be obtainediA；
Step 2) judging the LOS credibility based on a Wylie recognition algorithm, screening out base stations with high credibility as the basis of next distance reconstruction, wherein the step 2) comprises the following steps:
step 2.1) according to Wylie recognition algorithm, respectively calculating the variance of the positioning information from each base station channel
Figure BDA0003223836110000064
Wherein
Figure BDA0003223836110000065
For the variance, s, of channel positioning information from base station ii(t) is the distance measurement from time t to the ith base station, and ri(t) is the measurement after polynomial smoothing;
step 2.2) minimum variance
Figure BDA0003223836110000066
The corresponding base station is set as base station m with a variance of
Figure BDA0003223836110000067
Selecting four base stations with minimum variance as BSm，BS1，BS2，BS3Corresponding variance is
Figure BDA0003223836110000071
Figure BDA0003223836110000072
The relationship of size is
Figure BDA0003223836110000073
Base station BSm，BS1，BS2，BS3Has LOS reliability of BSm>BS1>BS2>BS3That is, the signal of the base station m is the highest reliability in the LOS environment;
step 3) reconstructing the distance information obtained in the step 1), wherein the reconstructed result is used for constructing a target equation set, and the step 3) comprises the following steps:
step 3.1) constructing new distance information by using ToA ranging information and AoA derived ranging information
di＝λi1diT+λi2diA
Wherein λi1+λi2＝1；
Step 3.2) according to the variance value of the positioning information in the channels m,1,2 and 3 of the base station obtained in the step 2), order
Figure BDA0003223836110000074
Wherein i ═ m,1,2, 3;
step 3.3) when
Figure BDA0003223836110000075
When each base station is affected by NLOS to approximate, then order
Figure BDA0003223836110000076
When in use
Figure BDA0003223836110000077
When the influence degree difference of each base station by NLOS is obvious, the order is
Figure BDA0003223836110000078
When in use
Figure BDA0003223836110000079
When in use, will
Figure BDA00032238361100000710
Scaling to intervals
Figure BDA00032238361100000711
At the same time
Figure BDA00032238361100000712
Scaling to equal degree, and returning to the step 3.3) for assignment;
step 4) according to the distance information obtained in the step, using a Talor-BFGS joint algorithm to carry out final position calculation, wherein the step 4) comprises the following steps:
step 4.1) iteration is carried out by using a Talor series expansion algorithm, wherein the TDOA equation set is obtained by the base station and distance reconstruction values obtained in the step 2) and the step 3), and an initial iteration value of the Talor series expansion algorithm is obtained by resolving through a ToA algorithm, so that the initial value is more accurate and more beneficial to the implementation of the iteration algorithm;
and 4.2) adopting the positioning information obtained by the Talor series expansion algorithm as an iteration initial value of a BFGS quasi-Newton iteration algorithm, performing BFGS quasi-Newton iteration, resolving by adopting a chain iteration mode, and outputting a final position.

Claims (3)

1. A UWB indoor positioning method based on LOS credibility identification is characterized by comprising the following steps:
step 1) resolving the distance from the UWB positioning tag to the base station by using a ToA algorithm and an AoA algorithm respectively, wherein the step 1) comprises the following steps:
step 1.1) obtaining the distance d between the positioning label and the base station i by using a ToA algorithmiT；
Step 1.2) using an AoA algorithm to calculate an included angle between the tag and the base station and calculating the distance d from the UWB tag to the base station iiA；
According to the sine theorem:
Figure FDA0003223836100000011
then
Figure FDA0003223836100000012
Wherein alpha isiAngle information obtained for AoA,/i,i+1Is the distance of base station i to i +1, and therefore
Figure FDA0003223836100000013
The distance d can be obtainediA；
Step 2) based on LOS reliability of Wylie recognition algorithm, the possibility of being judged as a line-of-sight environment can be obtained, and the step 2) comprises the following steps:
step 2.1) respectively calculating the variance of the positioning information from each base station channel according to the Wylie identification algorithm
Figure FDA0003223836100000014
Wherein
Figure FDA0003223836100000015
Locating information for i channel from base stationVariance of information, si(t) is the distance measurement from time t to the ith base station, and ri(t) is the measurement after polynomial smoothing;
step 2.2) minimum variance
Figure FDA0003223836100000021
The corresponding base station is set as base station m with a variance of
Figure FDA0003223836100000022
Selecting four base stations with minimum variance as BSm，BS1，BS2，BS3Corresponding variance is
Figure FDA0003223836100000023
Figure FDA0003223836100000024
The relationship of size is
Figure FDA0003223836100000025
Base station BSm，BS1，BS2，BS3Has LOS reliability of BSm>BS1>BS2>BS3That is, the signal of the base station m is the highest reliability in the LOS environment;
step 3) reconstructing the distance information obtained in step 1), wherein step 3) comprises the following steps:
step 3.1) constructing new distance information by using ToA ranging information and AoA derived ranging information
di＝λi1diT+λi2diA
Wherein λi1+λi2＝1；
Step 3.2) according to the variance value of the positioning information in the channels m,1,2 and 3 of the base station obtained in the step 2), order
Figure FDA0003223836100000026
Wherein i ═ m,1,2, 3;
step 3.3) when
Figure FDA0003223836100000027
When each base station is affected by NLOS to approximate, then order
Figure FDA0003223836100000028
When in use
Figure FDA0003223836100000029
When the influence degree difference of each base station by NLOS is obvious, the order is
Figure FDA00032238361000000210
When in use
Figure FDA00032238361000000211
When in use, will
Figure FDA00032238361000000212
Scaling to intervals
Figure FDA00032238361000000213
At the same time
Figure FDA00032238361000000214
Scaling to equal degree, and returning to the step 3.3) for assignment;
step 4) according to the distance information obtained in the step, using a Talor-BFGS joint algorithm to carry out final position calculation, wherein the step 4) comprises the following steps:
step 4.1) iteration is carried out by using a Talor series expansion algorithm, wherein the TDOA equation set is obtained by the base station and distance reconstruction value obtained in the step 2) and the step 3), and an iteration initial value of the Talor series expansion algorithm is obtained by resolving through a ToA algorithm;
and 4.2) adopting the positioning information obtained by the Talor series expansion algorithm as an iteration initial value of a BFGS quasi-Newton iteration algorithm to carry out BFGS quasi-Newton iteration and output a final position.
2. The UWB indoor positioning method based on LOS credibility identification of claim 1, wherein:
in the LOS confidence determination based on the Wylie identification algorithm in the step 2), the base station selection is performed according to the rank order of the measured noise variance in the Wylie identification algorithm, and the determination of NLOS is not performed.
3. The UWB indoor positioning method based on LOS credibility identification of claim 1, wherein:
the distance reconstruction in the step 3) is based on the ToA and AoA ranging data, wherein the AoA ranging data is calculated according to the measured angle and the distance between the base stations.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6526283B1 (en) * 1999-01-23 2003-02-25 Samsung Electronics Co, Ltd Device and method for tracking location of mobile telephone in mobile telecommunication network
CN101277527A (en) * 2008-05-09 2008-10-01 山东大学 Honeycomb net distance reconstructing algorithm
US20110019567A1 (en) * 2008-01-29 2011-01-27 Wenhua Jiao Method for positioning mobile devices and apparatus for positioning mobile devices
CN112904274A (en) * 2021-01-21 2021-06-04 中国人民解放军海军工程大学 Multi-moving-object positioning method for improving TDOA/FDOA algorithm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6526283B1 (en) * 1999-01-23 2003-02-25 Samsung Electronics Co, Ltd Device and method for tracking location of mobile telephone in mobile telecommunication network
US20110019567A1 (en) * 2008-01-29 2011-01-27 Wenhua Jiao Method for positioning mobile devices and apparatus for positioning mobile devices
CN101277527A (en) * 2008-05-09 2008-10-01 山东大学 Honeycomb net distance reconstructing algorithm
CN112904274A (en) * 2021-01-21 2021-06-04 中国人民解放军海军工程大学 Multi-moving-object positioning method for improving TDOA/FDOA algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘正波;朱亮, LOS/NLOS混合环境下的基于TOA测距的定位算法 *
甘宇, UWB室内定位算法研究 *

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