Automatic target recognition using high-resolution radar range-profiles

Recognition of aircraft and ground targets from high resolution radar (HRR) range-profiles is a notoriously difficult problem, due in large part to the extreme variability in the data for small changes in target orientation. To achieve recognition in the presence of this variability, the problem is posed in the context of joint tracking and recognition of a target using a sequence of observed HRR range-profiles. 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 Bayesian inference on the target type parameter and the sequence of target orientations at the observation times.; Successful recognition is critically dependent on an appropriate model for the HRR range-profiles. A deterministic model and a conditionally Gaussian model are introduced, and the likelihood functions under each model for varying orientations and target types are compared. The comparison is extended to include both aircraft and ground targets, different radar frequency bands, and full polarimetric range-profiles. The results of these comparisons show better performance for the conditionally Gaussian model in terms of the potential for correct recognition when the orientation estimate has significant error.; Fundamental limits on the performance of estimators of target orientation are obtained for the two models in terms of a Hilbert-Schmidt lower bound on the expected errors. The bound is evaluated for each model using simulated data as a function of the intensity of the noise in the observations. This analysis provides a specific criterion for model selection for this problem.; Simulations are presented in which each of the sensor models is combined with a constant orientation rate model for the target dynamics to produce an algorithm for joint tracking and recognition using HRR data. Results from the simulations show the performance of the algorithm in the presence of additive noise, including the expected angular estimation error and the probability of correct recognition. The algorithm using the conditionally Gaussian model achieves superior performance while requiring significantly less memory.