High resolution radar models for joint tracking and recognition

Identification of airborne or ground targets using high resolution radar (HRR) range-profiles is a notoriously difficult problem, due in large part to the extreme variability of the range-profile for small changes in target aspect angle. We address the problem of joint tracking and recognition of a target using a sequence of HRR range-profiles within a likelihood-based framework. The likelihood function for the scene configuration combines a dynamics-based prior on the sequence of target orientations with a likelihood for range-profiles given the target orientation. The recognition system performs joint inference on the target type parameter and the sequence of target orientations at the observation times. The primary issue with respect to successful recognition is modeling of the HRR data. The use of either deterministic or stochastic models for the range profiles is possible within our framework. A deterministic model and a conditionally Gaussian model for the range-profile are introduced, and the likelihood functions under each model for varying orientations and target types are compared. Simulations are presented demonstrating recognition of mobile ground targets within our framework. Results showing performance of the algorithm are given in terms of the expected angular estimation error and the rate of correct recognition