A novel cooperative localization algorithm using enhanced particle filter technique in maritime search and rescue wireless sensor network

被引:39
|
作者
Wu, Huafeng [1 ]
Mei, Xiaojun [1 ]
Chen, Xinqiang [1 ]
Li, Junjun [1 ]
Wang, Jun [2 ]
Mohapatra, Prasant [3 ]
机构
[1] Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China
[2] Univ Cent Florida, Orlando, FL 32836 USA
[3] Univ Calif Davis, Davis, CA 95616 USA
基金
中国国家自然科学基金;
关键词
Wireless sensor networks; Maritime search and rescue; Cooperative localization; Enhanced particle filter; Kullback-Leibler divergence; TRACKING;
D O I
10.1016/j.isatra.2017.09.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Maritime search and rescue (MSR) play a significant role in Safety of Life at Sea (SOLAS). However, it suffers from scenarios that the measurement information is inaccurate due to wave shadow effect when utilizing wireless Sensor Network (WSN) technology in MSR. In this paper, we develop a Novel Cooperative Localization Algorithm (NCLA) in MSR by using an enhanced particle filter method to reduce measurement errors on observation model caused by wave shadow effect. First, we take into account the mobility of nodes at sea to develop a motion model-Lagrangian model. Furthermore, we introduce both state model and observation model to constitute a system model for particle filter (PF). To address the impact of the wave shadow effect on the observation model, we develop an optimal parameter derived by Kullback-Leibler divergence (KLD) to mitigate the error. After the optimal parameter is acquired, an improved likelihood function is presented. Finally, the estimated position is acquired (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:39 / 46
页数:8
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