The Location Fingerprinting and Dead Reckoning Based Hybrid Indoor Positioning Algorithm

2015 ◽  
pp. 605-614 ◽  
Author(s):  
Ruiyun Yu ◽  
Pengfei Wang ◽  
Zhijie Zhao
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Location Fingerprinting ◽  
Positioning Algorithm

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

2020 ◽  
Vol 10 (2) ◽  
pp. 668 ◽  
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).

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

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