Stability evaluation and track life of the PDAF for tracking in clutter

The authors present an effective approach of a hybrid nature to the nonsimulation performance evaluation of the probabilistic data association filtering (PDAF) method for tracking in clutter. In this approach, a continuous-valued covariance, which is a function of a discrete-valued random variable (the number of validated measurements), is used to characterize the tracking errors in an average sense. This covariance can be calculated offline recursively from a modified Riccati equation, which can be obtained by replacing the measurement-dependent terms in the original stochastic equation with their conditional expectations. This approach has the merit that it yields a quantification of the transients of tracking divergence as well as substantially better accuracy than previous work. Such an approach is particularly suitable for stability evaluation of tracking filters. In addition, a quantitative study of the track-life problem is made in which the number of validated measurements plays a central role