An indoor fusion positioning algorithm of Bluetooth and PDR based on particle filter with dynamic adjustment of weights calculation strategy

Abstract The key to fingerprint positioning algorithm is establishing effective fingerprint information database based on different reference nodes of received signal strength indicator (RSSI). Traditional method is to set the location area calibration multiple information sampling points, and collection of a large number sample data what is very time consuming. With Zigbee sensor networks as platform, considering the influence of positioning signal interference, we proposed an improved algorithm of getting virtual database based on polynomial interpolation, while the pre-estimated result was disposed by particle filter. Experimental result shows that this method can generate a quick, simple fine-grained localization information database, and improve the positioning accuracy at the same time.

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Particle Filter and Clustering Based Robust Positioning Algorithm in the WiFi Weak Signal Environment

Proceedings of the 2017 International Technical Meeting of The Institute of Navigation ◽

10.33012/2017.14965 ◽

2017 ◽

Author(s):

Beomju Shin ◽

Bosun Yu ◽

Jaewon Bang ◽

Jeahun Kim ◽

Taikjin Lee

Keyword(s):

Particle Filter ◽

Weak Signal ◽

Signal Environment ◽

Positioning Algorithm

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The IMU/UWB Fusion Positioning Algorithm Based on a Particle Filter

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi6080235 ◽

2017 ◽

Vol 6 (8) ◽

pp. 235 ◽

Cited By ~ 11

Author(s):

◽

Keyword(s):

Particle Filter ◽

Positioning Algorithm

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Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

Applied Sciences ◽

10.3390/app10020668 ◽

2020 ◽

Vol 10 (2) ◽

pp. 668 ◽

Cited By ~ 3

Author(s):

Meng Sun ◽

Yunjia Wang ◽

Shenglei Xu ◽

Hongji Cao ◽

Minghao Si

Keyword(s):

Particle Filter ◽

Single Point ◽

Dead Reckoning ◽

Indoor Positioning ◽

Magnetic Field Data ◽

Geographic Coordinate System ◽

Positioning Method ◽

Positioning Algorithm ◽

Optimization Mechanism ◽

Degradation Problem

This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

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Research on Extended Kalman Filter and Particle Filter Combinational Algorithm in UWB and Foot-Mounted IMU Fusion Positioning

Mobile Information Systems ◽

10.1155/2018/1587253 ◽

2018 ◽

Vol 2018 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Xin Li ◽

Yan Wang ◽

Dawei Liu

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Extended Kalman Filter ◽

Particle Filters ◽

Range Data ◽

Kinematic Equation ◽

Positioning Method ◽

Nonlinear Condition ◽

Positioning Algorithm ◽

Range Error

As UWB high-precision positioning in NLOS environment has become one of the hot topics in the research of indoor positioning, this paper firstly presents a method for the smoothing of original range data based on the Kalman filter by the analysis of the range error caused by UWB signals in LOS and NLOS environment. Then, it studies a UWB and foot-mounted IMU fusion positioning method with the integration of particle filter with extended Kalman filter. This method adopts EKF algorithm in the kinematic equation of particle filters algorithm to calculate the position of each particle, which is like the way of running N (number of particles) extended Kalman filters, and overcomes the disadvantages of the inconformity between kinematic equation and observation equation as well as the problem of sample degeneration under the nonlinear condition of the standard particle filters algorithm. The comparison with the foot-mounted IMU positioning algorithm, the optimization-based UWB positioning algorithm, the particle filter-based UWB positioning algorithm, and the particle filter-based IMU/UWB fusion positioning algorithm shows that our algorithm works very well in LOS and NLOS environment. Especially in an NLOS environment, our algorithm can better use the foot-mounted IMU positioning trajectory maintained by every particle to weaken the influence of range error caused by signal blockage. It outperforms the other four algorithms described as above in terms of the average and maximum positioning error.

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