Automatic target recognition system using high resolution radar

A system is proposed for joint tracking and recognition of airborne targets from the observations of radar sensors. It is assumed that the data available for the estimation of target orientation and recognition include sequences of range-profiles from a high resolution radar. Inference is performed using the posterior distribution on the complete parameter space, which includes the number of targets as well as their positions, orientations, and target types. The algorithm is critically dependent on appropriate sensor and target models, in the form of a likelihood for the range profiles given the target orientations, and a prior on the orientations determined by the target dynamics. Deterministic and stochastic models for high resolution radar data are presented, and the likelihood function under the deterministic model is examined. The viability of our approach is demonstrated through simulations that address two simplified recognition scenarios. The first simulation investigates joint tracking and recognition of a single maneuvering target from the simulated observations of both a cross-array tracking radar and a high resolution radar. In the second simulation, orientation estimation and recognition are performed for a single target which is approaching an airborne radar platform. Results from these simulations showing performance are given.