Performance prediction of tracking in clutter with nearest neighbor filters

The measurement that is `closest' to the predicted target measurement is known as the `nearest neighbor' measurement in target tracking. A common method currently in wide use for tracking in clutter is the so-called nearest neighbor filter, which uses only the nearest neighbor measurement as if it is the true one. This paper presents a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of the nearest neighbor filter. This technique can quantify the dynamic process of tracking divergence as well as the steady state performance. The technique is based on a general approach to the performance prediction of algorithms with both continuous and discrete uncertainties developed recently by the authors.