CN111198365A - Indoor positioning method based on radio frequency signal - Google Patents

Indoor positioning method based on radio frequency signal Download PDF

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CN111198365A
CN111198365A CN202010049141.8A CN202010049141A CN111198365A CN 111198365 A CN111198365 A CN 111198365A CN 202010049141 A CN202010049141 A CN 202010049141A CN 111198365 A CN111198365 A CN 111198365A
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李建
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Dongfanghong Satellite Mobile Communication Co Ltd
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    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S11/00Systems for determining distance or velocity not using reflection or reradiation
    • G01S11/02Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements

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Abstract

The invention discloses an indoor positioning method based on radio frequency signals, which firstly shortens the online positioning time by constructing a fingerprint database in advance based on a position fingerprint positioning method, acquires the intensity of received signals to construct a fingerprint database, and determines the position of a terminal based on a fingerprint database matching method in an online stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.

Description

Indoor positioning method based on radio frequency signal
Technical Field
The invention relates to the technical field of indoor positioning, in particular to an indoor positioning method based on radio frequency signals.
Background
With the development of positioning technology, people are more and more widely used for positioning, and meanwhile, the requirement of people for positioning is continuously increased. However, most of the currently used positioning technologies are based on GPS services, and GPS signals cannot achieve positioning effects outdoors indoors, so that a new technology needs to be sought to improve the situation of poor indoor positioning accuracy.
Due to the rapid popularization of radio frequency equipment such as WLAN and the like, a hardware technical basis is laid for a positioning method based on radio frequency signals, and the rapid popularization and use are facilitated. At present, a positioning technology based on radio frequency signals at home and abroad has been developed primarily, the positioning technology based on position fingerprints is one of main research directions, and the position fingerprint positioning method is used for completing indoor positioning based on the mapping relation between received signal strength information and position information.
The establishment of the position fingerprint database is one of key technologies for realizing the method, generally, the position fingerprint database stores the received signal strength information acquired in an off-line stage, and the position fingerprint positioning determines the position of the terminal based on a KNN method by matching the radio frequency signal strength acquired in the on-line stage with the received signal strength information stored in the database. However, the signal transmission and reception are easily blocked, which causes errors between the signal strength acquired in real time and the signal in the database, and affects the real-time positioning accuracy of the terminal in the online stage.
Disclosure of Invention
To the deficiency of the prior art, the technical problem to be solved by the present patent application is: how to provide an indoor positioning method based on radio frequency signals, which can effectively estimate track information of a terminal, effectively reduce positioning errors caused by signal shielding and perform real-time positioning of the terminal in an environment with signal shielding.
In order to achieve the purpose, the invention adopts the following technical scheme:
an indoor positioning method based on radio frequency signals comprises the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i,yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i,yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', gamma ') by using a weighted KNN algorithm, entering a step S7 if the position fingerprint database does not contain terminal track information, and entering a step S3 if the position fingerprint database contains the terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1,γ1);
S4: based on terminal location (x)1,y1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2,y2);
S6: comparing the corrected predicted position information (x)2,y2) Comparing the error with a threshold value with the position information (x ', y') of the computing terminal, and if the error is higher than the threshold value, (x) is calculated2,γ2) As final terminal position information (x ', γ'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', y'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
Further, in step S1, the location fingerprint database RSS and location information (x)i,yi) The mapping model is as follows:
Figure BDA0002370502090000031
wherein, N is the number of the collected information, and K is the vector number of RSS.
Further, in step S1, the received signal strength information RSSiThe following were used:
Figure BDA0002370502090000032
further, in step S2, the weighted KNN algorithm has the following formula:
Figure BDA0002370502090000033
Figure BDA0002370502090000034
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri,cAre signals in the database.
Further, in step S3, the method for calculating the predicted terminal position by the autoregressive moving average model is as follows:
Pt=β1Pt-12Pt-2+…+βPPt-P+Zt
wherein, the track Pt=(xi,yi) β is the weight of the member star position at each time, and there is also a correlation between the error terms, denoted as Zt=εt1εt-12εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
Further, in step S4, based on the terminal position (x)1,γ1) Generating a population of particles (x, γ), said population generating method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1=h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj=g(RSSj-f(Gj))
wherein: f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weight are removed, and a new particle set G and a new weight w are obtained;
further, in step S5, the method for calculating the corrected predicted position includes:
Figure BDA0002370502090000041
the corrected predicted position (x)2,γ2) And calculating the terminal position (x)1,y1) The error calculation method is delta ═ PN+1-Pk|。
Compared with the prior art, the indoor positioning method based on the radio frequency signal has the following technical effects by adopting the technical scheme:
1) the invention relates to an indoor positioning method based on radio frequency signals, which comprises the steps of constructing a position fingerprint database by collecting received signal strength information and terminal position information in an off-line stage, and reducing positioning time in the on-line stage;
2) according to the indoor positioning method based on the radio frequency signals, in an online stage, an autoregressive sliding model is constructed based on the motion track of the terminal, the position of the terminal is predicted in advance, and positioning errors caused by signal shielding are reduced;
3) according to the indoor positioning method based on the radio frequency signal, the motion trail prediction method of the terminal is optimized based on the particle feedback method, the terminal position distribution is expanded by constructing the particle swarm, and the online positioning precision is improved;
4) the indoor positioning method based on the radio frequency signals calculates the terminal position based on the weighted KNN algorithm, obtains the terminal position by weighting through calculating the distance between the intensity of the real-time collected and received signals and the intensity of the signals stored in the database, and improves the precision of the positioning algorithm;
5) according to the indoor positioning method based on the radio frequency signals, the terminal position obtained through comparison calculation and the terminal position obtained through prediction are obtained, signal shielding information is obtained, and the influence of signal shielding on the positioning result is reduced.
The invention designs an indoor positioning method based on radio frequency signals, and aims to solve the problem that in the positioning process, when shielding exists between a signal source and a terminal, part of terminals cannot receive signals of a reference star, so that a positioning result has large errors. Aiming at the problem of signal shielding in positioning, the offset track is corrected by a particle filter feedback method based on the motion track of the terminal. Firstly, on-line positioning time is shortened by a mode of constructing a fingerprint library in advance based on a position fingerprint positioning method, received signal strength is collected to construct the fingerprint library, and the position of a terminal is determined based on a fingerprint library matching method in an on-line stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.
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FIG. 1 is a flow chart of an implementation of the present invention based on an indoor positioning system for RF signals;
FIG. 2 is a schematic diagram of the positioning of the indoor positioning system based on RF signals according to the present invention;
fig. 3 is a comparison diagram of the positioning correction result of the rf signal based indoor positioning system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1-2, an indoor positioning method based on radio frequency signals includes the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i,yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i,yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', y ') by using a weighted KNN algorithm, entering step S7 if the position fingerprint database does not contain terminal track information, and entering step S3 if the position fingerprint database contains terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1,γ1);
S4: based on terminal location (x)1,γ1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2,y2);
S6: comparing the corrected predicted position information (x)2,γ2) And calculating the terminal position information (x ', gamma'), comparing the error with a threshold, and if the error is higher than the threshold, (x) is calculated2,y2) As final terminal position information (x ', y'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', γ'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
In this embodiment, in step S1, the location fingerprint database RSS and location information (x)i,yi) The mapping model is as follows:
Figure BDA0002370502090000061
wherein, N is the number of the collected information, and K is the vector number of RSS.
In this embodiment, in step S1, the received signal strength information RSSiThe following were used:
Figure BDA0002370502090000071
in this embodiment, in step S2, the weighted KNN algorithm has the following formula:
Figure BDA0002370502090000072
Figure BDA0002370502090000073
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri,cAre signals in the database.
In this embodiment, in step S3, the method for calculating the predicted terminal position by the autoregressive moving average model is as follows:
Pt=β1Pt-12Pt-2+…+βPPt-P+Zt
wherein, the track Pt=(xi,yi) β is the weight of the member star position at each time, and there is also a correlation between the error terms, denoted as Zt=εt1εt-12εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
In this embodiment, in step S4, the terminal position (x) is used as the basis1,y1) Generating a particle population (x, y), the particle population generation method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1=h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj=g(RSSj-f(Gj))
wherein: f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weight are removed, and a new particle set G and a new weight W are obtained;
in this embodiment, in step S5, the method for calculating the corrected predicted position includes:
Figure BDA0002370502090000081
the corrected predicted position (x)2,γ2) And calculating the terminal position (x)1,y1) The error calculation method is delta ═ PN+1-Pk|。
Fig. 3 shows the bit error rate simulation result of an indoor positioning method based on radio frequency signals. As can be seen from the figure, the uncorrected positioning result has larger error due to shielding, the influence of the error can be smaller by adopting the fitting method, and the positioning precision loss caused by shielding can be better reduced by adopting the method provided by the invention.
The invention discloses an indoor positioning method based on radio frequency signals, which aims at the problem that when shielding exists between a signal source and a terminal in the positioning process, part of terminals cannot receive signals of a reference star, so that a positioning result has large errors. Aiming at the problem of signal shielding in positioning, the offset track is corrected by a particle filter feedback method based on the motion track of the terminal. Firstly, on-line positioning time is shortened by a mode of constructing a fingerprint library in advance based on a position fingerprint positioning method, received signal strength is collected to construct the fingerprint library, and the position of a terminal is determined based on a fingerprint library matching method in an on-line stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (7)

1. An indoor positioning method based on radio frequency signals is characterized by comprising the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i,yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i,yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', y ') by using a weighted KNN algorithm, entering step S7 if the position fingerprint database does not contain terminal track information, and entering step S3 if the position fingerprint database contains terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1,y1);
S4: based on terminal location (x)1,y1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2,y2);
S6: comparing the corrected predicted position information (x)2,y2) And calculating the terminal position information (x ', gamma'), comparing the error with a threshold, and if the error is higher than the threshold, (x) is calculated2,y2) As final terminal position information (x ', y'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', γ'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
2. The method as claimed in claim 1, wherein in step S1, the location fingerprint database RSS and location information (x)i,yi) The mapping model is as follows:
Figure FDA0002370502080000011
wherein, N is the number of the collected information, and K is the vector number of RSS.
3. The method as claimed in claim 1, wherein in step S1, the received signal strength information RSS isiThe following were used:
Figure FDA0002370502080000021
4. the method according to claim 3, wherein in step S2, the weighted KNN algorithm has the following formula:
Figure FDA0002370502080000022
Figure FDA0002370502080000023
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri,cAre signals in the database.
5. The method as claimed in claim 4, wherein in step S3, the calculation method for predicting the terminal position by the autoregressive moving average model is as follows:
Pt=β1Pt-12Pt-2+…+βPPt-P+Zt
wherein, the track Pt=(xi,yi) β is the weight of the member star position at each moment, and there is a correlation between the error terms, expressed as
Zt=εt1εt-12εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
6. The method as claimed in claim 5, wherein the step S4 is based on terminal location (x)1,y1) Generating a particle population (x, y), the particle population generation method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1=h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj=g(RSSj-f(Gj))
wherein: and f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weights are removed, and a new particle set G and a new weight W are obtained.
7. The method as claimed in claim 6, wherein in step S5, the method for calculating the corrected predicted position comprises:
Figure FDA0002370502080000031
the corrected predicted position (x)2,y2) And calculating the terminal position (x)1,y1) The error calculation method comprises
δ=|PN+1-Pk|。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113810854A (en) * 2021-09-16 2021-12-17 中国联合网络通信集团有限公司 Terminal motion track determination method and server

Citations (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2840250A1 (en) * 2011-06-30 2013-01-03 Trusted Positioning Inc. An improved system and method for wireless positioning in wireless network-enabled environments
EP2724174A2 (en) * 2011-06-21 2014-04-30 BAE Systems Plc Tracking algorithm
KR20140146879A (en) * 2013-06-18 2014-12-29 한국항공대학교산학협력단 Method for estimating indoor position based on wireless lan, sever and terminal
CN104703143A (en) * 2015-03-18 2015-06-10 北京理工大学 Indoor positioning method based on WIFI signal strength
EP3016458A1 (en) * 2014-10-30 2016-05-04 Alcatel Lucent Apparatus, Mobile Device, Base Station Transceiver, Adaptation Server, Method and Computer Program for providing information related to a predicted channel state
CN105792356A (en) * 2016-04-22 2016-07-20 西安理工大学 Wifi-based location fingerprint positioning method
CN105933858A (en) * 2016-03-14 2016-09-07 上海剑桥科技股份有限公司 Wireless positioning device
CN106153049A (en) * 2016-08-19 2016-11-23 北京羲和科技有限公司 A kind of indoor orientation method and device
KR20170004556A (en) * 2015-07-03 2017-01-11 한국과학기술원 Method and apparatus for relacation of mobile robot in indoor environment
CN106646366A (en) * 2016-12-05 2017-05-10 深圳市国华光电科技有限公司 Visible light positioning method and system based on particle filter algorithm and intelligent equipment
CN107246873A (en) * 2017-07-03 2017-10-13 哈尔滨工程大学 A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter
CN107246872A (en) * 2017-06-28 2017-10-13 东南大学 Single-particle filtering guider and method based on MEMS sensor and VLC positioning fusions
CN107948930A (en) * 2017-12-31 2018-04-20 电子科技大学 Indoor positioning optimization method based on location fingerprint algorithm
MY165778A (en) * 2010-01-21 2018-04-25 Univ Tenaga Nasional Fluctuation correction for k-nearest neighbor location fingerprinting for indoor positioning system
CN108161882A (en) * 2017-12-08 2018-06-15 华南理工大学 A kind of robot teaching reproducting method and device based on augmented reality
CN108168563A (en) * 2018-02-08 2018-06-15 西安建筑科技大学 A kind of megastore's indoor positioning air navigation aid based on WiFi
CN109298389A (en) * 2018-08-29 2019-02-01 东南大学 Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method
CN110037336A (en) * 2019-04-19 2019-07-23 浙江中烟工业有限责任公司 A kind of prediction technique of Cigarette circumference control system executing agency position
CN110321902A (en) * 2019-05-09 2019-10-11 哈尔滨工业大学 A kind of indoor automatic vision fingerprint collecting method based on SOCP
CN110333479A (en) * 2019-07-09 2019-10-15 东华大学 It is a kind of based on the wireless location method for improving particle filter under complex indoor environment
CN110533166A (en) * 2019-08-21 2019-12-03 中山大学 It is a kind of based on when space fusion feature indoor orientation method

Patent Citations (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
MY165778A (en) * 2010-01-21 2018-04-25 Univ Tenaga Nasional Fluctuation correction for k-nearest neighbor location fingerprinting for indoor positioning system
EP2724174A2 (en) * 2011-06-21 2014-04-30 BAE Systems Plc Tracking algorithm
CA2840250A1 (en) * 2011-06-30 2013-01-03 Trusted Positioning Inc. An improved system and method for wireless positioning in wireless network-enabled environments
KR20140146879A (en) * 2013-06-18 2014-12-29 한국항공대학교산학협력단 Method for estimating indoor position based on wireless lan, sever and terminal
EP3016458A1 (en) * 2014-10-30 2016-05-04 Alcatel Lucent Apparatus, Mobile Device, Base Station Transceiver, Adaptation Server, Method and Computer Program for providing information related to a predicted channel state
CN104703143A (en) * 2015-03-18 2015-06-10 北京理工大学 Indoor positioning method based on WIFI signal strength
KR20170004556A (en) * 2015-07-03 2017-01-11 한국과학기술원 Method and apparatus for relacation of mobile robot in indoor environment
CN105933858A (en) * 2016-03-14 2016-09-07 上海剑桥科技股份有限公司 Wireless positioning device
CN105792356A (en) * 2016-04-22 2016-07-20 西安理工大学 Wifi-based location fingerprint positioning method
CN106153049A (en) * 2016-08-19 2016-11-23 北京羲和科技有限公司 A kind of indoor orientation method and device
CN106646366A (en) * 2016-12-05 2017-05-10 深圳市国华光电科技有限公司 Visible light positioning method and system based on particle filter algorithm and intelligent equipment
CN107246872A (en) * 2017-06-28 2017-10-13 东南大学 Single-particle filtering guider and method based on MEMS sensor and VLC positioning fusions
CN107246873A (en) * 2017-07-03 2017-10-13 哈尔滨工程大学 A kind of method of the mobile robot simultaneous localization and mapping based on improved particle filter
CN108161882A (en) * 2017-12-08 2018-06-15 华南理工大学 A kind of robot teaching reproducting method and device based on augmented reality
CN107948930A (en) * 2017-12-31 2018-04-20 电子科技大学 Indoor positioning optimization method based on location fingerprint algorithm
CN108168563A (en) * 2018-02-08 2018-06-15 西安建筑科技大学 A kind of megastore's indoor positioning air navigation aid based on WiFi
CN109298389A (en) * 2018-08-29 2019-02-01 东南大学 Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method
CN110037336A (en) * 2019-04-19 2019-07-23 浙江中烟工业有限责任公司 A kind of prediction technique of Cigarette circumference control system executing agency position
CN110321902A (en) * 2019-05-09 2019-10-11 哈尔滨工业大学 A kind of indoor automatic vision fingerprint collecting method based on SOCP
CN110333479A (en) * 2019-07-09 2019-10-15 东华大学 It is a kind of based on the wireless location method for improving particle filter under complex indoor environment
CN110533166A (en) * 2019-08-21 2019-12-03 中山大学 It is a kind of based on when space fusion feature indoor orientation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
IANG, M., HUANG, Z., LI, J. ET: "Indoor anti-occlusion visible light positioning systems based on particle filtering" *
王屹进: "基于多元数据融合的室内定位算法的研究" *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113810854A (en) * 2021-09-16 2021-12-17 中国联合网络通信集团有限公司 Terminal motion track determination method and server
CN113810854B (en) * 2021-09-16 2023-10-03 中国联合网络通信集团有限公司 Method for determining motion trail of terminal and server

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