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Sensors 2018, 18(9), 2945; https://doi.org/10.3390/s18092945

A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks

Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China
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Received: 8 August 2018 / Revised: 25 August 2018 / Accepted: 1 September 2018 / Published: 4 September 2018
(This article belongs to the Special Issue Applications of Wireless Sensors in Localization and Tracking)
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Abstract

Wireless sensor networks (WSNs) have become a popular research subject in recent years. With the data collected by sensors, the information of a monitored area can be easily obtained. As a main contribution of WSN localization is widely applied in many fields. However, when the propagation of signals is obstructed there will be some severe errors which are called Non-Line-of-Sight (NLOS) errors. To overcome this difficulty, we present a residual analysis-based improved particle filter (RAPF) algorithm. Because the particle filter (PF) is a powerful localization algorithm, the proposed algorithm adopts PF as its main body. The idea of residual analysis is also used in the proposed algorithm for its reliability. To test the performance of the proposed algorithm, a simulation is conducted under several conditions. The simulation results show the superiority of the proposed algorithm compared with the Kalman Filter (KF) and PF. In addition, an experiment is designed to verify the effectiveness of the proposed algorithm in an indoors environment. The localization result of the experiment also confirms the fact that the proposed algorithm can achieve a lower localization error compared with KF and PF. View Full-Text
Keywords: wireless sensor network; non-line of sight error; mobile localization; particle filter; residual analysis wireless sensor network; non-line of sight error; mobile localization; particle filter; residual analysis
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Cheng, L.; Feng, L.; Wang, Y. A Residual Analysis-Based Improved Particle Filter in Mobile Localization for Wireless Sensor Networks. Sensors 2018, 18, 2945.

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