Open AccessTarget tracking based on improved square root cubature particle filter via underwater wireless sensor networks
Author(s) -
Feng Hailin,
Cai Zhiwei
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5097
Subject(s) - tracking (education) , computer science , wireless sensor network , particle filter , square root , degeneracy (biology) , fuse (electrical) , energy consumption , filter (signal processing) , sensor fusion , algorithm , underwater , real time computing , control theory (sociology) , artificial intelligence , computer vision , mathematics , engineering , computer network , psychology , pedagogy , oceanography , geometry , control (management) , geology , bioinformatics , electrical engineering , biology
Target tracking in underwater wireless sensor networks (UWSNs) has two fundamental issues that tracking accuracy and energy consumption. Although the application of particle filter in target tracking is considered in recent years, degeneracy phenomenon and particle impoverishment always restrict its capacity and application. This paper proposes an improved square root cubature particle filter (ISRCPF) to improve tracking accuracy. The authors employ self‐adaptive artificial fish swarm algorithm (AFSA) to optimise the particles, which makes the particles move towards to high likelihood region and maintains the diversity of the particles. Moreover, a sensor selection scheme is provided, which reduces energy consumption of the network by exactly waking up four sensor nodes at each time, while preserving tracking accuracy. Additionally, the authors propose a novel fusion method called similarity fusion method (SFM) to fuse local estimates together and then obtain a better result for distributed fusion architecture (DFA). The simulation results demonstrate that the proposed methods have superior performance.