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access icon free Joint registration and multi-target tracking based on labelled random finite set and expectation maximisation

© The Institution of Engineering and Technology
Received 03/04/2017, Accepted 18/11/2017, Revised 27/10/2017, Published 23/11/2017

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.

Inspec keywords: target tracking; set theory; recursive estimation; expectation-maximisation algorithm

Other keywords: observation uncertainty; measurement accumulation; target state estimation; expectation maximisation approach; data log-likelihood function; numerical simulations; labelled random finite set; state RFS variables; time-varying biases; analytical algorithm; joint sensor registration and multitarget tracking; sensor biases estimation; EM approach

Subjects: Signal processing theory; Combinatorial mathematics; Combinatorial mathematics; Other topics in statistics; Other topics in statistics; Signal processing and detection

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