A novel cooperative localization algorithm using enhanced particle filter technique in maritime search and rescue wireless sensor network. (July 2018)

• Record Type: Journal Article
• Authors: Wu, Huafeng; Mei, Xiaojun; Chen, Xinqiang; Li, Junjun; Wang, Jun; Mohapatra, Prasant
• Is Part Of: ISA transactions. Volume 78(2018)
• Page Start: 39
• Page End: 46
• Publication Date: 2018-07
• Subjects: Wireless sensor networks -- Maritime search and rescue -- Cooperative localization -- Enhanced particle filter -- Kullback-Leibler divergence; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2017.09.013
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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 Highlights: A mobility model is introduced to describe the motion of nodes at sea. An optimal parameter deriving by Kullback-Leibler divergence is proposed to reduce the deviation caused by wave shadow effect at sea. After acquirement of optimal parameter, a novel cooperative localization based on using enhanced particle filter is presented. Comparison with other algorithms, the performance of proposed method is ideal at sea.