Bias change detection‐based sensor selection approach for target tracking in large‐scale distributed sensor networks. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Guo, Junjun; Yuan, Xianghui; Han, Chongzhao
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 1(2017)
• Page Start: 30
• Page End: 39
• Publication Date: 2017-01-01
• Subjects: target tracking -- sensor fusion -- signal detection -- filtering theory -- distributed sensors; bias change detection‐based sensor selection approach -- target tracking -- large‐scale distributed sensor networks -- sensor bias -- bias change detection‐based SS approach -- multisensor target tracking problem -- data fusion -- sliding window marginalised likelihood ratio test algorithm -- federated filter algorithm -- bias registration; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2015.0498
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In large‐scale distributed sensor networks, in the presence of the possible changing sensor biases, this study proposes a bias change detection‐based sensor selection (SS) approach to address this multi‐sensor target tracking problem. The authors only need to detect the bias change and select those reliable sensors (sensors with constant biases) for data fusion, rather than the traditional approach, which try to directly estimate the changing biases. The proposed method mainly contains the following three steps. First, the sliding window marginalised likelihood ratio test algorithm is proposed to detect the bias change. Then based on the bias change detection results, they adopt an SS strategy to select those reliable sensors. Finally, for these selected distributed sensors, the federated filter algorithm is used here for target tracking and bias registration. Simulation results show the effectiveness of these algorithms.