FEATURE EXTRACTION AND OPTIMIZATION OF REPRESENTATIVE-SLICE IN AMBIGUITY FUNCTION FOR MOVING RADAR EMITTER RECOGNITION

Radar emitter recognition is an important and challenging subject in radar signal analysis and processing. In this work, an ambiguity function (AF) representative-slice based feature extraction and optimization algorithm is presented for unintentional modulation recognition of moving radar emitters. It considers near-zero slices of AF as representative feature set of radar emitters, which not only coincides with the characteristics of real radar signals, but also mitigates the computation problem and avoids undesired cross terms in existing AF based method. Direct Discriminant Ratio (DDR) criterion is further utilized to preserve the most discriminant features and boost recognition accuracy, by ranking the kernel points along the representative-slice. Experimental results validate the practical usefulness and high stability of the proposed approach on real data of moving radar emitters, as well as synthetic radar data from U. S. Naval Research Laboratory.