Raytheon Company

Raquel E Maderazo

Theagenis John Abatzoglou

Frederick A Dominski

Jessica E Swanson

A radar classifies an unknown target illuminated with a large bandwidth pulse. The large bandwidth pulse may be algorithmically synthesized. The target reflects the large bandwidth pulse to form a return. The return is digitized into digital samples at range bin intervals. A computer extracts unknown range and amplitude pairs descriptive of the unknown target from the digital samples. Some range and amplitude pairs are located within one range bin interval. Principle scatterers are extracted from the unknown range and amplitude pairs using Modified Forward backward linear Prediction to form an unknown feature vector for the target. A plurality of pre-stored, known feature vectors containing known range and amplitude pairs are retrieved from the computer. The known range and amplitude pairs are descriptive of known targets, and are grouped in clusters having least dispersion for each of the known targets. The computer associates, for the principal scatterers, the unknown feature vector descriptive of the unknown target with each of the known feature vectors. The target is classified by using highest a posteriori conditional probability density obtained from comparing the known feature vectors with the unknown feature vector. The principal scatterers descriptive of the unknown, target are estimated using a Modified Forward Backward Linear Prediction. The Modified Forward Backward Linear Prediction also estimates range of the principal scatterers forming the unknown target. The principal scatterers are tested for decaying modes. The Modified Forward Backward Linear Prediction estimates are evaluated using Cramer Reo Bound computation for robustness.

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Marina Del Rey, CA, United States of America

Raquel E Maderazo