CN106767815A - Weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance - Google Patents

Weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance Download PDF

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Publication number
CN106767815A
CN106767815A CN201611081340.7A CN201611081340A CN106767815A CN 106767815 A CN106767815 A CN 106767815A CN 201611081340 A CN201611081340 A CN 201611081340A CN 106767815 A CN106767815 A CN 106767815A
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label
reference label
phase difference
distance
reader
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CN106767815B (en
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马永涛
苗新龙
高政
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Tianjin University
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Tianjin University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • 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

Abstract

The present invention discloses a kind of weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance, including：Arrangement positioning RFID reader and reference label；The distance between phase difference estimation reader and label for being received using reader, the range error that multipath effect, white Gaussian noise cause is equivalent to Gaussian Profile, the average of deviation and variance constitute the location parameter and scale parameter of Gaussian Profile parameter between the estimated distance and actual range between reference label and each reader；Reference label is estimated with tag distances to be positioned；Using the inverse of the variance of distance estimations error between label to be positioned and reader, reference label as weighted factor, orientation problem is solved with weighted least square algorithm.The characteristics of present invention has precision higher.

Description

Weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance
Technical field
The present invention relates to a kind of indoor positioning problem.
Background technology
Location technology brings great convenience for the life of people, recently as the fast development of Internet of Things, people To the demand more and more higher for positioning, the application of indoor positioning technologies is also more extensive.Satellite-based location technology and based on shifting The location technology of dynamic Cellular Networks is influenceed very big by building, in the scene of the building Relatively centralized such as school, warehouse, market Positioning precision will be substantially reduced, it is impossible to meet high-precision location requirement.
In positioning indoors, electromagnetic wave runs into ground, ceiling, wall, barrier etc. in transmitting procedure can be occurred instead Penetrate and produce multipath effect, multipath effect causes the information such as energy, phase, the time of signal that receiver receives to change Become, so as to produce serious interference to positioning result.
In current location technology, mainly there are infrared confirming orientation technology, ultrasonic wave indoor positioning technologies, fixed in bluetooth room Position technology, WIFI indoor positioning technologies, ZigBee indoor positioning technologies, computer vision location technology, UWB indoor positioning Technology, UHF RFID indoor positioning technologies etc..Wherein, the location technology based on RFID has low cost, high precision, real-time Advantage, and fingerprint base need not be pre-build, the workload of later maintenance is smaller.
The content of the invention
It is smaller it is an object of the invention to provide a kind of maintenance workload, precision indoor positioning algorithms higher.Technology Scheme is as follows：
A kind of weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance, comprise the following steps：
1) positioning scene is arranged：At least three RFID readers are arranged in the diverse location of the scene of required positioning, and is spread If reference label；
2) reader is estimated with tag distances to be positioned：The phase difference estimation reader and label received using reader The distance between, the range error that multipath effect, white Gaussian noise cause is equivalent to Gaussian Profile, reference label is read with each Read device between estimated distance and actual range between deviation average and variance constitute Gaussian Profile parameter location parameter and Scale parameter；
3) reference label is estimated with tag distances to be positioned：The phase difference matrix of label is constructed, between reference label Actual range is fitted with phase difference Euclidean distance, solves dij=aEijThe undetermined coefficient a and b of+b, wherein, dijIt is Actual range between i reference label and j-th reference label, EijFor between i-th reference label and j-th reference label Phase difference Euclidean distance；Range error that multipath effect, the fitting of phase difference Euclidean distance and white Gaussian noise are caused etc. It is Gaussian Profile to imitate, between any one reference label and other reference labels by the estimated distance of linear fit with it is actual away from The average and variance of the deviation between constitute the location parameter and scale parameter of Gaussian Profile parameter；
4) using between label to be positioned and reader, reference label the variance of distance estimations error it is reciprocal as weight because Son, orientation problem is solved with weighted least square algorithm.
The present invention can alleviate the influence of multipath effect and white Gaussian noise to positioning result in indoor positioning, it is not necessary to build The distance between vertical fingerprint base, it is not required that understand the concrete condition of environment in advance, reference label and label to be positioned estimate profit It is fitted with phase difference Euclidean distance, the distance estimations that multipath effect, white Gaussian noise, the fitting of phase difference Euclidean distance cause is missed Difference is equivalent to Gaussian Profile, then solves orientation problem with weighted least-squares, improves the precision of positioning.
Brief description of the drawings
Fig. 1 shows positioning scene schematic diagram of the present invention.
Fig. 2 shows reader distance evaluated error curve.
Fig. 3 shows reference label distance estimations error curve.
Fig. 4 shows positioning performance comparison diagram.
Specific embodiment
The validity of algorithm is put forward for checking, the present invention has carried out experiment simulation in matlab.Wherein, multipath channel mould Type uses statistical channel model, the maximum multipath number of the signal that reader is received to change successively from 3 to 10, is positioned at 10 ×10m2Band positioning region carry out, it is as follows in this embodiment concrete application：
1. positioning scene is arranged.Four readers are disposed in four corners of area to be targeted, equidistant laying 9 on ground Reference label, as shown in Figure 1.
2. reader is estimated with tag distances to be positioned.The phase difference estimation reader and label received using reader The distance between, the range error that multipath effect, white Gaussian noise cause is equivalent to Gaussian Profile, reference label is read with each Read device between estimated distance and actual range between deviation average and variance constitute Gaussian Profile parameter location parameter and Scale parameter.
3. reference label is estimated with tag distances to be positioned.The phase difference matrix of label is constructed, between label i and label j Phase difference Euclidean distance beTo actual range and phase difference between reference label Euclidean distance is fitted, and solves dij=aEijThe undetermined coefficient a and b of+b.Multipath effect, phase difference Euclidean distance are intended Close, the range error that white Gaussian noise causes is equivalent to Gaussian Profile, between any one reference label and other reference labels The position of Gaussian Profile parameter is constituted by the average and variance of the deviation between the estimated distance and actual range of linear fit Parameter and scale parameter.
4. distance estimations error is equivalent to the feasibility of Gaussian Profile for checking.When maximum multipath number is 7, with reading The probability density curve of distance estimations error and the positional information and phase information using reference label between device, reference label The Gaussian distribution curve of calculating is approached, and as shown in Figures 2 and 3, it is feasible that distance estimations error is equivalent into Gaussian Profile.
5. regard reader and reference label as reference mode, have N number of reference mode, can be with by step 2 and step 3 Calculate the location parameter λ of the distance estimations Gaussian Profile parameter between reference modeiWith scale parameter σi, with reference mode it Between distance be estimated as di, i=1 ..., N.Then have
The derivation of equation can obtain following formula：
WA θ=Wb
Wherein,
Calculating above formula can try to achieve the coordinate θ=(A of label to be positionedTWA)-1ATWb。

Claims (1)

1. a kind of weighted least-squares indoor positioning algorithms based on the range finding of phase difference Euclidean distance, comprise the following steps：
1) positioning scene is arranged：At least three RFID readers are arranged in the diverse location of the scene of required positioning, and lays ginseng Examine label；
2) reader is estimated with tag distances to be positioned：Between the phase difference estimation reader and label that are received using reader Distance, the range error that multipath effect, white Gaussian noise cause is equivalent to Gaussian Profile, reference label and each reader Between estimated distance and actual range between deviation average and variance constitute Gaussian Profile parameter location parameter and yardstick Parameter；
3) reference label is estimated with tag distances to be positioned：The phase difference matrix of label is constructed, to the reality between reference label Distance is fitted with phase difference Euclidean distance, solves dij=aEijThe undetermined coefficient a and b of+b, wherein, dijIt is i-th Actual range between reference label and j-th reference label, EijFor between i-th reference label and j-th reference label Phase difference Euclidean distance；The range error that multipath effect, the fitting of phase difference Euclidean distance and white Gaussian noise are caused is equivalent It is Gaussian Profile, by the estimated distance and actual range of linear fit between any one reference label and other reference labels Between deviation average and variance constitute Gaussian Profile parameter location parameter and scale parameter；
4) using between label to be positioned and reader, reference label the variance of distance estimations error inverse as weighted factor, Orientation problem is solved with weighted least square algorithm.
CN201611081340.7A 2016-11-30 2016-11-30 Weighted least-squares indoor orientation method based on phase difference Euclidean distance ranging Active CN106767815B (en)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107341424A (en) * 2017-06-28 2017-11-10 西安交通大学 A kind of precise phase computational methods based on the estimation of RFID multipaths
CN108089149A (en) * 2017-12-19 2018-05-29 成都鸿福润德科技有限公司 A kind of ultra wide band location method based on signal two-way transmission time
CN110109054A (en) * 2019-04-03 2019-08-09 佛山市顺德区中山大学研究院 A kind of RFID localization method and device based on phase difference correction
CN110207699A (en) * 2018-02-28 2019-09-06 北京京东尚科信息技术有限公司 A kind of localization method and device
CN110850401A (en) * 2019-08-27 2020-02-28 天津大学 RFID label positioning method based on motion model and synthetic aperture
CN111505572A (en) * 2020-04-07 2020-08-07 电子科技大学 RFID moving track detection method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009063114A1 (en) * 2007-11-16 2009-05-22 Universidad De Malaga Rfid-based object tracking for the visually impaired
CN102111876A (en) * 2011-02-24 2011-06-29 华为技术有限公司 Method and device for selecting reference labels used for location

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009063114A1 (en) * 2007-11-16 2009-05-22 Universidad De Malaga Rfid-based object tracking for the visually impaired
CN102111876A (en) * 2011-02-24 2011-06-29 华为技术有限公司 Method and device for selecting reference labels used for location

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘熙等: "多径环境下无源超高频RFID定位算法研究", 《计算机工程》 *
马永涛等: "A Multipath Mitigation Localization Algorithm Based on MDS for Passive UHF RFID", 《IEEE COMMUNICATIONS LETTERS》 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107341424A (en) * 2017-06-28 2017-11-10 西安交通大学 A kind of precise phase computational methods based on the estimation of RFID multipaths
CN108089149A (en) * 2017-12-19 2018-05-29 成都鸿福润德科技有限公司 A kind of ultra wide band location method based on signal two-way transmission time
CN110207699A (en) * 2018-02-28 2019-09-06 北京京东尚科信息技术有限公司 A kind of localization method and device
CN110207699B (en) * 2018-02-28 2022-04-12 北京京东尚科信息技术有限公司 Positioning method and device
CN110109054A (en) * 2019-04-03 2019-08-09 佛山市顺德区中山大学研究院 A kind of RFID localization method and device based on phase difference correction
CN110850401A (en) * 2019-08-27 2020-02-28 天津大学 RFID label positioning method based on motion model and synthetic aperture
CN110850401B (en) * 2019-08-27 2022-06-28 天津大学 RFID label positioning method based on motion model and synthetic aperture
CN111505572A (en) * 2020-04-07 2020-08-07 电子科技大学 RFID moving track detection method
CN111505572B (en) * 2020-04-07 2023-03-10 电子科技大学 RFID (radio frequency identification) moving track detection method

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