State dependent detection and object tracking

Target tracking algorithms usually treat the probability of detection as a constant, independent of the target state. In most cases this is not true, one obvious example being the Doppler frequency based clutter rejection, the other is obfuscation (shadowing) of ground based targets. This dependency modulates the measurement likelihood, which in turn introduces measurement non-linearity. In this paper we first present a general algorithm for target tracking in clutter when the probability of detection is target state dependent, and then proceed to an algorithm where both target state estimate and the probability of detection are modeled as Gaussian Mixtures. Probability of target existence is recursively updated as the track quality measure used for false track discrimination. A two sensor based ground target tracking in clutter simulation validates this approach.