Theoretical analysis and performance prediction of tracking in clutter with strongest neighbor filters

A simple and commonly used method for tracking in clutter is the so-called strongest neighbor filter (SNF), which uses the “strongest neighbor” measurement, that is, the one with the strongest intensity (amplitude) in the neighborhood of the predicted target measurement, as if it were the true one. The purpose of this paper is two-fold. First, the following theoretical results of tracking in clutter with SNF are derived: the a priori probabilities of data association events and the one-step prediction of the matrix mean square error conditioned on these events. Secondly, a technique for prediction without recourse to expensive Monte Carlo simulation of the performance of SNF is presented. This technique can quantify the dynamic process of tracking divergence as well as the steady state performance. The technique is a new development along the line of the recently developed general approach prediction of algorithms with both continuous and discrete uncertainties