Diffusion analysis of track loss in clutter

In tracking a target through clutter, the selection of incorrect measurements for track updating causes track divergence and eventual loss of track. The plot-to-track association algorithm is modeled as a Markov process and the tracking error is modeled as a diffusion process in order to study the mechanism of track loss analytically, without recourse to Monte Carlo simulations, for nearest-neighbor association in two space dimensions. The time evolution of the error distribution is examined, and the connection of the approach with diffusion theory is discussed. Explicit results showing the dependence of various performance parameters, such as mean time to lose track and track half-life, on the clutter spatial density are presented. The results indicate the existence of a critical density region in which the tracking performance degrades rapidly with increasing clutter density. An optimal gain adaptation procedure that significantly improves the tracking performance in the critical region is proposed