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Sensors 2016, 16(6), 786; doi:10.3390/s16060786

Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks

1,2,†
,
3,* , 1,2,†
and
3
1
The State Monitoring Center and Testing Center, Beijing 100037, China
2
Shenzhen Institute of Radio Testing & Tech., Shenzhen 518000, China
3
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Davide Brunelli
Received: 27 February 2016 / Revised: 17 May 2016 / Accepted: 20 May 2016 / Published: 30 May 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [694 KB, uploaded 30 May 2016]   |  

Abstract

Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. View Full-Text
Keywords: particle filter; nonparametric estimation; indoor positioning; wireless sensor networks; Kullback–Leibler divergence; dynamic Gaussian model particle filter; nonparametric estimation; indoor positioning; wireless sensor networks; Kullback–Leibler divergence; dynamic Gaussian model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Zhao, Y.; Zhang, S.; Fan, X. Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks. Sensors 2016, 16, 786.

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