Radar tracking of a move-Stop-move Maneuvering Target in Clutter

In this paper we compare three different sequential estimation algorithms for tracking a single move-stop-move radar target in clutter. We consider optimal and suboptimal Bayesian estimation algorithms, with a special focus on particle filters (PF). The target is modeled using Markov Chains switching theory. Target maneuvers are defined by four different motion models: a stopped target model, a constant velocity model, an acceleration and a deceleration model. We analyze a realistic car traffic scenario by splitting the problem into two study cases. In the first case measurements are expressed in Cartesian coordinates, while in the second we address the problem of nonlinearity in the measurement model. Both cases are characterized by the presence of additive Gaussian noise and by a detection probability less than unity. In addition we are also interested in false measurements originated by high level clutter. The aim of this paper is to compare the so called IMM-PDA-ABF (interacting multiple model, probabilistic data association, auxiliary bootstrap filter) to the well-established Kalman-based PDAF (probabilistic data association filter) and IMM-PDAF (interacting multiple model, probabilistic data association filter) tracking algorithms. Parametric and non-parametric sequential estimation procedures are also taken into account. Advantages and disadvantages of the proposed algorithms are illustrated and discussed through computer simulations.