A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

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.