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 PDFInfo
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- 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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- 238000005259 measurement Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 description 2
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Classifications
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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
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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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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
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
Technical Field
The invention relates to an indoor positioning problem.
Background
The positioning technology brings great convenience to the life of people, along with the rapid development of the Internet of things, the requirement of people on positioning is higher and higher in recent years, and the application of the indoor positioning technology is wider. The positioning technology based on the satellite and the positioning technology based on the mobile cellular network are greatly influenced by buildings, the positioning accuracy in relatively concentrated scenes of buildings such as schools, warehouses, markets and the like can be greatly reduced, and the high-accuracy positioning requirement cannot be met.
In indoor positioning, electromagnetic waves encounter the ground, the ceiling, the wall, obstacles and the like in the transmission process and are reflected to generate a multipath effect, and the multipath effect causes the information such as energy, phase, time and the like of signals received by a receiver to be changed, so that serious interference is generated on the positioning result.
In the current positioning technology, there are mainly an infrared positioning technology, an ultrasonic indoor positioning technology, a bluetooth indoor positioning technology, a WIFI indoor positioning technology, a ZigBee indoor positioning technology, a computer vision positioning technology, an ultra wide band indoor positioning technology, a UHF RFID indoor positioning technology, and the like. The RFID-based positioning technology has the advantages of low cost, high precision and strong real-time performance, a fingerprint library does not need to be established in advance, and the workload of later maintenance is small.
Disclosure of Invention
The invention aims to provide an indoor positioning algorithm which is small in maintenance workload and high in precision. The technical scheme is as follows:
a weighted least square indoor positioning algorithm based on phase difference Euclidean distance ranging comprises the following steps:
1) arranging a positioning scene: arranging at least three RFID readers at different positions of a scene to be positioned, and paving reference labels;
2) estimating the distance between the reader and the tag to be positioned: estimating the distance between the reader and the tag by using the phase difference received by the reader, equating the ranging error caused by multipath effect and Gaussian white noise to Gaussian distribution, and forming the position parameter and the scale parameter of a Gaussian distribution parameter by referring to the mean value and the variance of the deviation between the estimated distance between the tag and each reader and the actual distance;
3) estimating the distance between the reference tag and the tag to be positioned: constructing a phase difference matrix of the tags for the actual distance between reference tagsFitting with phase difference Euclidean distance to obtain dij＝a·EijA and b, where dijIs the actual distance between the ith and jth reference tags, EijThe Euclidean distance of the phase difference between the ith reference label and the jth reference label; the method comprises the steps that a distance measurement error caused by multipath effect, phase difference Euclidean distance fitting and Gaussian white noise is equivalent to Gaussian distribution, and the mean value and the variance of deviations between an estimated distance and an actual distance between any one reference label and other reference labels through linear fitting form position parameters and scale parameters of Gaussian distribution parameters;
4) and taking the reciprocal of the variance of the distance estimation errors between the label to be positioned and the reader and the reference label as a weighting factor, and solving the positioning problem by using a weighted least square algorithm.
The method can relieve the influence of multipath effect and white Gaussian noise on the positioning result in indoor positioning, does not need to establish a fingerprint database and does not need to know the specific situation of the environment in advance, the distance estimation between the reference label and the label to be positioned utilizes phase difference Euclidean distance fitting to equate the distance estimation error caused by multipath effect, white Gaussian noise and phase difference Euclidean distance fitting to Gaussian distribution, and then the positioning problem is solved by using weighted least square, so that the positioning precision is improved.
Drawings
Fig. 1 shows a schematic view of the inventive positioning scenario.
Fig. 2 shows a reader distance estimation error curve.
Fig. 3 shows a reference tag distance estimation error curve.
Fig. 4 shows a positioning performance comparison graph.
Detailed Description
In order to verify the effectiveness of the algorithm, the invention carries out experimental simulation in matlab, wherein, the multipath channel model adopts a statistical channel model, the maximum multipath number of the signals received by the reader is changed from 3 to 10 in sequence and is positioned at 10 × 10m2The tape positioning area of (2) is specifically applied as follows in the present embodiment:
1. and arranging a positioning scene. Four readers are arranged at four corners of an area to be positioned, and 9 reference tags are laid on the ground at equal intervals, as shown in FIG. 1.
2. And estimating the distance between the reader and the tag to be positioned. The distance between the reader and the tag is estimated by utilizing the phase difference received by the reader, the ranging error caused by multipath effect and Gaussian white noise is equivalent to Gaussian distribution, and the mean value and the variance of the deviation between the estimated distance between the reference tag and each reader and the actual distance form the position parameter and the scale parameter of the Gaussian distribution parameter.
3. And estimating the distance between the reference label and the label to be positioned. Constructing a phase difference matrix of the tags, wherein the Euclidean distance of the phase difference between the tag i and the tag j isFitting the actual distance between the reference labels and the phase difference Euclidean distance to solve dij＝a·EijThe undetermined coefficients a and b of + b. The distance measurement error caused by multipath effect, phase difference Euclidean distance fitting and Gaussian white noise is equivalent to Gaussian distribution, and the mean value and the variance of the deviation between the estimated distance and the actual distance between any one reference label and other reference labels through linear fitting form the position parameter and the scale parameter of a Gaussian distribution parameter.
4. And verifying the feasibility of equating the distance estimation error to a Gaussian distribution. When the maximum number of multipaths is 7, a probability density curve of a distance estimation error between the reader and the reference tag is close to a gaussian distribution curve calculated using the position information and the phase difference information of the reference tag, as shown in fig. 2 and 3, it is possible to equate the distance estimation error to a gaussian distribution.
5. The reader and the reference label are taken as reference nodes, N reference nodes are in total, and the position parameter lambda of the distance estimation Gaussian distribution parameter between the reader and the reference nodes can be calculated through the step 2 and the step 3iAnd the scale parameter σiAnd the distance from the reference node is estimated as diI is 1, …, N. Then there is
The formula derivation yields the following:
WAθ＝Wb
wherein,
calculating the coordinate theta of the label to be positioned by the formula (A)TWA)-1ATWb。
Claims (1)
1. A weighted least square indoor positioning algorithm based on phase difference Euclidean distance ranging comprises the following steps:
1) arranging a positioning scene: arranging at least three RFID readers at different positions of a scene to be positioned, and paving reference labels;
2) estimating the distance between the reader and the tag to be positioned: estimating the distance between the reader and the tag by using the phase difference received by the reader, equating the ranging error caused by multipath effect and Gaussian white noise to Gaussian distribution, and forming the position parameter and the scale parameter of a Gaussian distribution parameter by referring to the mean value and the variance of the deviation between the estimated distance between the tag and each reader and the actual distance;
3) estimating the distance between the reference tag and the tag to be positioned: constructing a phase difference matrix of the labels, fitting the actual distance between the reference labels and the Euclidean distance of the phase difference, and solving dij＝a·EijA and b, where dijIs the actual distance between the ith and jth reference tags, EijThe Euclidean distance of the phase difference between the ith reference label and the jth reference label; the method comprises the steps that a distance measurement error caused by multipath effect, phase difference Euclidean distance fitting and Gaussian white noise is equivalent to Gaussian distribution, and the mean value and the variance of deviations between an estimated distance and an actual distance between any one reference label and other reference labels through linear fitting form position parameters and scale parameters of Gaussian distribution parameters;
4) and taking the reciprocal of the variance of the distance estimation errors between the label to be positioned and the reader and the reference label as a weighting factor, and solving the positioning problem by using a weighted least square algorithm.
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Cited By (7)
| 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 |
| CN114280539A (en) * | 2021-12-27 | 2022-04-05 | 中国民航大学 | Positioning method based on missing feature vector estimation |
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Cited By (11)
| 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 |
| CN114280539A (en) * | 2021-12-27 | 2022-04-05 | 中国民航大学 | Positioning method based on missing feature vector estimation |
| CN114280539B (en) * | 2021-12-27 | 2024-08-06 | 中国民航大学 | Positioning method based on missing feature vector estimation |
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| CN106767815B (en) | 2019-09-03 |
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