Sensor selection based on maximum entropy fuzzy clustering for target tracking in large‐scale sensor networks. Issue 5 (1st July 2017)

• Record Type: Journal Article
• Authors: Guo, Junjun; Yuan, Xianghui; Han, Chongzhao
• Is Part Of: IET signal processing. Volume 11:Issue 5(2017)
• Page Start: 613
• Page End: 621
• Publication Date: 2017-07-01
• Subjects: sensor fusion -- fuzzy set theory -- pattern clustering -- maximum entropy methods -- target tracking -- distributed sensors; sensor selection strategies -- maximum entropy fuzzy clustering -- target tracking problem -- large‐scale sensor networks -- sensor‐level tracking -- data association problem -- global‐level fusion -- fusion centre -- track‐to‐track fusion -- bias registration -- data fusion -- sparse target environment -- dense target environment -- fuzzy membership reconstruction strategy; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2016.0306
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : This study proposes a sensor selection approach based on maximum entropy fuzzy clustering to address the target tracking problem in large‐scale sensor networks. The authors try to deal with this problem at two levels: (i) sensor‐level tracking: data association problem and sensor‐level tracking are carried out at the local level, and only the track outputs are transmitted to the fusion centre for data fusion; (ii) global‐level fusion: two sensor selection strategies are adopted at the fusion centre, which seek to only choose a subset of reliable sensors for track‐to‐track fusion and bias registration. In addition, an improved sensor selection approach is proposed for data fusion in both sparse and dense target environments, and a new fuzzy membership reconstruction strategy is introduced for data association in dense target environments. Furthermore, the proposed sensor selection strategy is also effective in the presence of the possible changing sensor biases. Simulation results are given to evaluate the performance of the proposed approaches.