CN111256695A - UWB/INS combined indoor positioning method based on particle filter algorithm - Google Patents

UWB/INS combined indoor positioning method based on particle filter algorithm Download PDF

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CN111256695A
CN111256695A CN202010034636.3A CN202010034636A CN111256695A CN 111256695 A CN111256695 A CN 111256695A CN 202010034636 A CN202010034636 A CN 202010034636A CN 111256695 A CN111256695 A CN 111256695A
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uwb
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particle filter
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CN111256695B (en
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唐普英
顾灵茹
刘平
邓佳坤
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University of Electronic Science and Technology of China
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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
    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention relates to a UWB/INS combined indoor positioning method based on a particle filter algorithm. Aiming at the condition that a UWB (ultra wide band) ranging result is easily interfered by a complex environment to influence the positioning precision in a non-line-of-sight environment, a UWB/INS combined indoor positioning method based on a particle filter algorithm is provided. The distance from the person to be positioned to each reference base station is obtained through a UWB system, and the east direction position and the north direction position of the person are calculated through a UWB position calculating unit. Three-axis acceleration, three-axis angular velocity and three-axis magnetic field intensity of a person in a walking process are obtained through an Inertial Navigation System (INS), and a step length, an eastern walking speed, a northern walking speed, a walking time and an attitude angle of the person in the walking process are calculated through an INS resolving unit. And performing data fusion on the calculation result of the UWB system and the calculation result of the INS system through a particle filter algorithm. Finally, the purposes of reducing the influence of a non-line-of-sight complex environment and improving the positioning precision are achieved.

Description

UWB/INS combined indoor positioning method based on particle filter algorithm
Technical Field
The invention relates to the technical field of indoor positioning, in particular to a UWB/INS combined indoor positioning method based on a particle filter algorithm.
Background
Today, the GPS satellite navigation positioning technology is developed more perfectly, and it is difficult to perform accurate tracking and positioning indoors and underground due to the characteristics of closed and complex indoor environment and underground environment. Therefore, the indoor positioning technology is produced at the same time.
Among the various indoor positioning schemes, much has focused on the study of a single positioning technology. A relatively good wireless location technology is the UWB indoor location technology, but UWB location is susceptible to environmental influences. If the indoor environment is open and free of interference of obstacles, namely, the UWB positioning accuracy is high in the line-of-sight environment; when the indoor environment is complex, namely under the non-line-of-sight environment, the UWB signal is affected by the multipath effect, so that the target cannot be accurately and continuously positioned.
The INS positioning technology is not easily influenced by the environment, and can continuously position and track the target. However, the INS positioning technique has accumulated errors and requires uninterrupted position correction.
The indoor positioning technology based on UWB/INS can not only improve the average positioning accuracy of the system, but also realize the long-time continuous positioning and tracking of the target.
Disclosure of Invention
The invention provides a UWB/INS combined indoor positioning method based on a particle filter algorithm, and aims to solve the technical problem that the positioning accuracy of a positioning system is not high due to a non-line-of-sight complex environment.
The technical scheme of the invention is as follows: an UWB/INS combined indoor positioning method based on a particle filter algorithm. The method comprises the following steps:
the distance from the person to be positioned to each reference base station is obtained through a UWB system, and the east direction position and the north direction position of the person are calculated through a UWB position calculating unit.
The method comprises the steps of obtaining three-axis acceleration, three-axis angular velocity and three-axis magnetic field intensity of a person in a walking process through an INS system, and calculating step length, eastern walking speed, northern walking speed, walking time and attitude angle of the person in the walking process through an INS resolving unit.
And establishing a particle filter model by taking the east position and the north position calculated by the UWB system, the east pace and the north pace calculated by the INS system as state vectors and the step length and the attitude angle calculated by the INS system as observation vectors.
And performing particle filtering processing to obtain the best indoor personnel position information at the current moment.
Further, the state equation of the particle filter is as follows:
Figure BDA0002365558520000021
wherein [ E (k +1) N (k +1) VE(k+1) VN(k+1)]And
[E(k) N(k) VE(k) VN(k)]the system comprises an east direction position, a north direction position, an east direction pace and a north direction pace which are respectively calculated by a UWB system and an INS system at the moment k +1 and the moment k, wherein T (k) is the step time of a person at the moment k, and omega (k) is system noise at the moment k.
Further, the observation equation of the particle filter is as follows:
Figure BDA0002365558520000022
Figure BDA0002365558520000023
Figure BDA0002365558520000024
h3＝1/VE(k)
wherein [ E (k) N (k) S (k) Y (k)]Is east direction position, north direction position, step length and attitude angle calculated by UWB system and INS system at time k, [ E (k) N (k) VE(k) VN(k)]The system is characterized in that the UWB system and the INS system calculate at the time k to obtain the east direction position, the north direction position, the east direction pace and the north direction pace, and gamma (k) is system observation noise at the time k.
Further, establishing a particle filter model, and executing a particle filter algorithm to process data, specifically:
initialization: extracting initialization states from a prior distribution
Figure BDA0002365558520000025
Is the state quantity of the ith particle of the 0 th sample point.
Sampling of a particle set:
Figure BDA0002365558520000026
Zkis the observed quantity of the 1 st to k th sampling points.
Calculating the importance weight of the particles:
Figure BDA0002365558520000027
normalization weight:
Figure BDA0002365558520000031
where N is the number of particles of the particle filter.
Selecting resampling: according to the normalized weight
Figure BDA0002365558520000032
Size of (2) to the particle set
Figure BDA0002365558520000033
And carrying out copying and elimination. And reset the weight
Figure BDA0002365558520000034
And (3) outputting: the particle filter outputs a set of sample points, approximately represented as a posterior distribution:
Figure BDA0002365558520000035
Xk＝∫Xkp(X0:k|Z1:k)dX0:k
and finally obtaining the best indoor personnel position estimation at the k moment.
The invention has the beneficial effects that: the data fusion is carried out on the results obtained by respective calculation of the UWB system and the INS system by using a particle filter algorithm, so that the influence of a non-line-of-sight complex environment on the positioning accuracy of the combined positioning system is reduced, and the optimal indoor personnel position estimation is obtained. Can be used for indoor high-precision personnel positioning.
Drawings
FIG. 1 is a schematic diagram of a UWB/INS combined indoor positioning system;
FIG. 2 is a schematic diagram of a UWB/INS combined indoor positioning method based on a particle filter algorithm.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific embodiments:
the UWB/INS combined indoor positioning method based on the particle filter algorithm comprises the following steps:
the method comprises the following steps of (1) carrying out indoor UWB base station arrangement in advance, fixing a UWB tag on a person, fixing an INS sensor on the tiptoe of the person, and then carrying out the following steps:
the method comprises the following steps: the distance from the person to be positioned to each reference base station is obtained through a UWB system, and the east direction position and the north direction position of the person are calculated through a UWB position calculating unit.
Step two: the method comprises the steps of obtaining three-axis acceleration, three-axis angular velocity and three-axis magnetic field intensity of a person in a walking process through an INS system, and calculating step length, eastern walking speed, northern walking speed, walking time and attitude angle of the person in the walking process through an INS resolving unit.
Step three: and establishing a particle filter model by taking the east position and the north position calculated by the UWB system, the east pace and the north pace calculated by the INS system as state vectors and the step length and the attitude angle calculated by the INS system as observation vectors. The method comprises the following steps:
the state equation of the particle filter is as follows:
Figure BDA0002365558520000041
wherein [ E (k +1) N (k +1) VE(k+1) VN(k+1)]And [ E (k) N (k) VE(k) VN(k)]The UWB system and the INS system calculate the east position, the north position, the east pace and the north direction at the moment k +1 and the moment k respectivelyPace, T (k), is the person's step time at time k, and ω (k) is the system noise at time k.
The observation equation of the particle filter is as follows:
Figure BDA0002365558520000042
Figure BDA0002365558520000043
Figure BDA0002365558520000044
h3＝1/VE(k)
wherein [ E (k) N (k) S (k) Y (k)]Is east direction position, north direction position, step length and attitude angle calculated by UWB system and INS system at time k, [ E (k) N (k) VE(k) VN(k)]The system is characterized in that the UWB system and the INS system calculate at the time k to obtain the east direction position, the north direction position, the east direction pace and the north direction pace, and gamma (k) is system observation noise at the time k.
Step four: and C, performing particle filter processing according to the particle filter model constructed in the step three, wherein the particle filter processing comprises the following steps:
initialization: extracting initialization states from a prior distribution
Figure BDA0002365558520000045
Is the state quantity of the ith particle of the 0 th sample point.
Sampling of a particle set:
Figure BDA0002365558520000046
Zkis the observed quantity of the 1 st to k th sampling points.
Calculating the importance weight of the particles:
Figure BDA0002365558520000047
normalization weight:
Figure BDA0002365558520000051
where N is the number of particles of the particle filter.
Selecting resampling: according to the normalized weight
Figure BDA0002365558520000052
Size of (2) to the particle set
Figure BDA0002365558520000053
And carrying out copying and elimination. And reset the weight
Figure BDA0002365558520000054
And (3) outputting: the particle filter outputs a set of sample points, approximately represented as a posterior distribution:
Figure BDA0002365558520000055
Xk＝∫Xkp(X0:k|Z1:k)dX0:k
and finally obtaining the best indoor personnel position estimation at the k moment.

Claims (4)

1. The UWB/INS combined indoor positioning method based on the particle filter algorithm is characterized by comprising the following steps:
the method comprises the following steps: acquiring the distance from a person to be positioned to each reference base station through a UWB system, and calculating the east position and the north position of the person through a UWB position calculating unit;
step two: acquiring three-axis acceleration, three-axis angular velocity and three-axis magnetic field intensity of a person in a walking process through an INS system, and calculating a step length, an eastern walking speed, a northern walking speed, a walking time and an attitude angle of the person in the walking process through an INS resolving unit;
step three: the east and north positions calculated by the UWB system and the east and north pace calculated by the INS system are used as state vectors, and the step length and attitude angle calculated by the INS system are used as observation vectors to construct a particle filter model;
step four: and performing particle filtering processing to obtain the best indoor personnel position estimation at the current moment.
2. The UWB/INS combined indoor positioning method based on particle filtering algorithm of claim 1, wherein the state equation of the particle filter is:
Figure FDA0002365558510000011
wherein [ E (k +1) N (k +1) VE(k+1) VN(k+1)]And [ E (k) N (k) VE(k) VN(k)]The system comprises an east direction position, a north direction position, an east direction pace and a north direction pace which are respectively calculated by a UWB system and an INS system at the moment k +1 and the moment k, wherein T (k) is the step time of a person at the moment k, and omega (k) is system noise at the moment k.
3. The UWB/INS combined indoor positioning method based on particle filter algorithm as set forth in claim 1, wherein the observation equation of the particle filter is:
Figure FDA0002365558510000012
Figure FDA0002365558510000013
Figure FDA0002365558510000014
h3＝1/VE(k)
wherein [ E (k) N (k) S (k) Y (k)]Is east direction position, north direction position, step length and attitude angle calculated by UWB system and INS system at time k, [ E (k) N (k) VE(k) VN(k)]The east position calculated by the UWB system and the INS system at the time k,North position, east pace, north pace, gamma (k) is the system observation noise at time k.
4. The UWB/INS combined indoor positioning method based on particle filter algorithm as claimed in claim 1, wherein the particle filter model is established, and the particle filter algorithm is executed to process data, specifically:
initialization: extracting initialization states from a prior distribution
Figure FDA0002365558510000021
The state quantity of the ith particle which is an initial sampling point;
sampling of a particle set:
Figure FDA0002365558510000022
Zkobserved quantities of 1 st to k th sampling points;
calculating the importance weight of the particles:
Figure FDA0002365558510000023
normalization weight:
Figure FDA0002365558510000024
wherein N is the number of particles of the particle filter;
selecting resampling: according to the normalized weight
Figure FDA0002365558510000025
Size of (2) to the particle set
Figure FDA0002365558510000026
Copy and discard, and reset the weights
Figure FDA0002365558510000027
And (3) outputting: the particle filter outputs a set of sample points, approximately represented as a posterior distribution:
Figure FDA0002365558510000028
Xk＝∫Xkp(X0:k|Z1:k)dX0:k
and finally obtaining the best indoor personnel position estimation at the k moment.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112556689A (en) * 2020-10-30 2021-03-26 郑州联睿电子科技有限公司 Positioning method integrating accelerometer and ultra-wideband ranging
CN113074739A (en) * 2021-04-09 2021-07-06 重庆邮电大学 UWB/INS fusion positioning method based on dynamic robust volume Kalman
CN113932809A (en) * 2021-11-26 2022-01-14 昆山九毫米电子科技有限公司 Indoor unmanned target vehicle positioning method based on intelligent particle filtering
CN114111802A (en) * 2021-12-21 2022-03-01 中国有色金属长沙勘察设计研究院有限公司 Pedestrian dead reckoning assisted UWB positioning method
CN116761254A (en) * 2023-08-17 2023-09-15 中国电信股份有限公司 Indoor positioning method, device, communication equipment and storage medium

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012072957A1 (en) * 2010-12-01 2012-06-07 Commissariat à l'énergie atomique et aux énergies alternatives Method and system for estimating a path of a mobile element or body
US20140195149A1 (en) * 2013-01-10 2014-07-10 Xue Yang Positioning and mapping based on virtual landmarks
CN105588566A (en) * 2016-01-08 2016-05-18 重庆邮电大学 Indoor positioning system and method based on Bluetooth and MEMS (Micro-Electro-Mechanical Systems) fusion
CN105928518A (en) * 2016-04-14 2016-09-07 济南大学 Indoor pedestrian UWB/INS tightly combined navigation system and method adopting pseudo range and position information
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
CN106680765A (en) * 2017-03-03 2017-05-17 济南大学 INS/UWB pedestrian navigation system and method based on distributed combined filter
WO2017139432A1 (en) * 2016-02-09 2017-08-17 5D Robotics, Inc. Ultra wide band radar localization
CN107990900A (en) * 2017-11-24 2018-05-04 江苏信息职业技术学院 A kind of particle filter design methods of pedestrian's indoor positioning data
CN109269498A (en) * 2018-08-06 2019-01-25 济南大学 Towards auto-adaptive estimate EKF filtering algorithm and system with shortage of data UWB pedestrian navigation
CN110426040A (en) * 2019-07-08 2019-11-08 中国人民解放军陆军工程大学 Indoor pedestrian's localization method with non line of sight identification function

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012072957A1 (en) * 2010-12-01 2012-06-07 Commissariat à l'énergie atomique et aux énergies alternatives Method and system for estimating a path of a mobile element or body
US20140195149A1 (en) * 2013-01-10 2014-07-10 Xue Yang Positioning and mapping based on virtual landmarks
CN105588566A (en) * 2016-01-08 2016-05-18 重庆邮电大学 Indoor positioning system and method based on Bluetooth and MEMS (Micro-Electro-Mechanical Systems) fusion
WO2017139432A1 (en) * 2016-02-09 2017-08-17 5D Robotics, Inc. Ultra wide band radar localization
CN105928518A (en) * 2016-04-14 2016-09-07 济南大学 Indoor pedestrian UWB/INS tightly combined navigation system and method adopting pseudo range and position information
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
CN106680765A (en) * 2017-03-03 2017-05-17 济南大学 INS/UWB pedestrian navigation system and method based on distributed combined filter
CN107990900A (en) * 2017-11-24 2018-05-04 江苏信息职业技术学院 A kind of particle filter design methods of pedestrian's indoor positioning data
CN109269498A (en) * 2018-08-06 2019-01-25 济南大学 Towards auto-adaptive estimate EKF filtering algorithm and system with shortage of data UWB pedestrian navigation
CN110426040A (en) * 2019-07-08 2019-11-08 中国人民解放军陆军工程大学 Indoor pedestrian's localization method with non line of sight identification function

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
QINGLIN TIAN: "A Resetting Approach for INS and UWB Sensor Fusion Using Particle Filter for Pedestrian Tracking", 《IEEE TRANSAC09 DECEMBER 2019TIONS ON INSTRUMENTATION AND MEASUREMENT 》 *
QINGLIN TIAN: "An INS and UWB Fusion Approach With Adaptive Ranging Error Mitigation for Pedestrian Tracking", 《IEEE SENSORS JOURNAL》 *
YUAN XU: "Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering", 《IEEE/CAA JOURNAL OF AUTOMATICA SINICA》 *
晁正正: "基于UWB/INS的单兵班组协同定位算法研究", 《测试技术学报》 *
朱彩杰: "UWB/MEMS室内定位技术研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 *
艾明曦: "基于低成本INS/RFID的室内定位技术", 《计算机测量与控制》 *
逯峤: "SR-CDKF在组合导航直接法滤波中的应用", 《弹道学报》 *
马宇: "基于UWB与RFID室内联合定位技术研究", 《万方数据库》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112556689A (en) * 2020-10-30 2021-03-26 郑州联睿电子科技有限公司 Positioning method integrating accelerometer and ultra-wideband ranging
CN112556689B (en) * 2020-10-30 2023-09-05 郑州联睿电子科技有限公司 Positioning method integrating accelerometer and ultra-wideband ranging
CN113074739A (en) * 2021-04-09 2021-07-06 重庆邮电大学 UWB/INS fusion positioning method based on dynamic robust volume Kalman
CN113932809A (en) * 2021-11-26 2022-01-14 昆山九毫米电子科技有限公司 Indoor unmanned target vehicle positioning method based on intelligent particle filtering
CN113932809B (en) * 2021-11-26 2024-03-12 昆山九毫米电子科技有限公司 Indoor unmanned target vehicle positioning method based on intelligent particle filtering
CN114111802A (en) * 2021-12-21 2022-03-01 中国有色金属长沙勘察设计研究院有限公司 Pedestrian dead reckoning assisted UWB positioning method
CN116761254A (en) * 2023-08-17 2023-09-15 中国电信股份有限公司 Indoor positioning method, device, communication equipment and storage medium
CN116761254B (en) * 2023-08-17 2023-11-07 中国电信股份有限公司 Indoor positioning method, device, communication equipment and storage medium

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