Feature-aided tracking with GMTI and HRR measurements via mixture density estimation

摘要

Tracking ground moving targets with ground moving target indicator and high resolution range (HRR) measurements is becoming increasingly important for many military and civilian applications. We first propose a new HRR information exploitation method using the technique of mixture density estimation. With this technique, features extracted from HRR profiles include not only peak locations and magnitudes, but also the information regarding how energy spreads around peaks. Therefore it is expected to increase significantly the feature discrimination power. We then develop a feature-aided tracking (FAT) algorithm that combines HRR features with traditional kinematic measurements in a probabilistic way. The algorithm does not require any a priori knowledge of target identifications. Simulation results are presented for both the HRR feature extraction method and the FAT algorithm.