Abstract
A prior knowledge of the background parameters such as clutter rate and detection profile is of critical importance in the tracking algorithms under the theory of random finite sets for extended objects which would lead to restrictions in the application. To accommodate this problem, a multiple extended target tracking algorithm based on the generalized labelled multi-Bernoulli (GLMB) filter under the circumstance of unknown clutter rate and detection profile is proposed in this article. After introducing a clutter generator, this new algorithm establishes augmented state space model for targets and clutter and propagates them in parallel by applying multi-class GLMB theory. We then employ Beta to describe detection probability. Target extension is modelled as an ellipse by using gamma Gaussian inverse Wishart distribution. Simulation results indicate that the proposed algorithm has better performance in estimating trajectories and extended shapes compared with the conventional filter having prior knowledge.
Highlights
Multiple target tracking (MTT) aims to estimate the exact number of targets and the states of each target from a stack of measurements with unknown sources
Taking the numerical complexity of Bayes multi-target filter into consideration, the Probability Hypothesis Density (PHD) [9] filter, Cardinalized PHD (CPHD) [10] filter and multi-target multi-Bernoulli (MeMBer) filter [11] have been developed as approximations
Notion of labelled Random Finite Sets (RFS) is introduced and the analytic solution to address target trajectories known as the generalized labelled multi-Bernoulli (GLMB) filter is proposed by Vo in [12] and [13]
Summary
Multiple target tracking (MTT) aims to estimate the exact number of targets and the states of each target from a stack of measurements with unknown sources. After introducing labelled RFS method into this problem, an efficient implementation of GLMB for point targets with unknown background parameters is described in [19] and it shows better robustness in high clutter rate or low detection probability situations compared with CPHD filter in [17]. We propose an extended target GLMB filter that can adaptively learn the unknown clutter rate and detection profile while tracking.
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