ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.161
Distributed Gaussian Particle Filter for Target Tracking in Wireless Sensor Networks
Abstract— In this paper, we present a distributed Gaussian particle filter based on Mahalanobis distance
(DGPF-MD) for target tracking in wireless sensor networks. The proposed algorithm consists of three major
steps. First, a sensor selection scheme is performed to reduce the cost of transmission among sensors with
high accuracy. Second, a distributed Gaussian particle filter is adopted for each selected sensor to estimate
the local statistics. Third, during weighted average fusion, the global estimate is based on the utility of the
data provided by the member sensors, which is characterized as MD between the sensor and predicted target
position. Compared with the centralized particle filters (CPFs), our experimental evaluations show that the
DGPF-MD has more acceptable complexity, lower communication cost, and shorter tracking latency.
Index Terms— Wireless sensor network, Gaussian particle filtering, distributed particle filter, target tracking,
Mahalanobis distance
Xuguang Yang, Yuechuan Zhang, Xukang Wu, Lianhai Shan, Yunzhou Qiu, Chunlei Zheng
Shanghai Internet of Things Co., LTD, CHINA
Cite: Xuguang Yang, Yuechuan Zhang, Xukang Wu, Lianhai Shan, Yunzhou Qiu, Chunlei Zheng, "Distributed Gaussian Particle Filter for Target Tracking in Wireless Sensor Networks," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 928-934, Beijing, 25-27 June, 2017.