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

An effective hybrid approach to the performance evaluation of the probabilistic data association (PDA) method for tracking in clutter is presented. 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 expected values. This approach has the merit of yielding a quantification of the transients of tracking divergence, as well as better accuracy than previous work. Such an approach is particularly suitable for stability studies of tracking filters. In addition, a quantitative study of the track life problem is conducted, in which the number of validated measurements plays a central role.