Joint registration and multi‐target tracking based on labelled random finite set and expectation maximisation. Issue 3 (3rd January 2018)

• Record Type: Journal Article
• Authors: Li, Minzhe; Jing, Zhongliang; Pan, Han; Dong, Peng
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 3(2018)
• Page Start: 312
• Page End: 322
• Publication Date: 2018-01-03
• Subjects: target tracking -- set theory -- expectation‐maximisation algorithm -- recursive estimation; joint sensor registration and multitarget tracking -- labelled random finite set -- expectation maximisation approach -- analytical algorithm -- observation uncertainty -- data log‐likelihood function -- state RFS variables -- EM approach -- sensor biases estimation -- target state estimation -- measurement accumulation -- time‐varying biases -- numerical simulations; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0137
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : A new analytical algorithm is developed to address the problem of joint sensor registration and multi‐target tracking with varying target number and observation uncertainty based on the labelled random finite set (RFS) and expectation maximisation (EM). A new complete data log‐likelihood function is derived with the measurement and state RFS variables, and the EM approach is employed to jointly estimate the sensor biases and target states. Moreover, a recursive implementation is provided to deal with the measurement accumulation, and the situation of the time‐varying biases is handled. Since the estimates of the bias and state are analytically calculated, the performance of the proposed method is better than that of the traditional methods. The effectiveness and superiority of the proposed algorithm are verified using numerical simulations.