CPHD and PHD filters for unknown backgrounds, part III: tractable multitarget filtering in dynamic clutter

Abstract

In a previous conference paper the first author addressed the problem of devising CPHD and PHD filters that are capable of multitarget detection and tracking in unknown, dynamically changing clutter. That paper assumed that the clutter process is Poisson with an intensity function that is a finite mixture with unknown parameters. The measurement-update equations for these CPHD/PHD filters involved combinatorial sums over all partitions of the current measurement-set. This paper describes an approach that avoids combinatorial sums and is therefore potentially computationally tractable. Clutter is assumed to be a binomial i.i.d. cluster process with unknown parameters. Given this, three different and successively more tractable CPHD/PHD filters are derived, all capable of multitarget track-before-detect capability. The first assumes that the entire intensity function of the clutter process is unknown. The second and third assume that the clutter spatial distribution is known but that the clutter rate (number of clutter returns per scan) is unknown.