Abstract:
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 ...Show MoreMetadata
Abstract:
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.
Published in: 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Date of Conference: 20-22 August 2008
Date Added to IEEE Xplore: 10 October 2008
ISBN Information: