Optimization of time and frequency resolution for radar transmitter identification

An entirely new set of criteria for the design of kernels for time-frequency representations (TFRs) has been previously proposed. The goal of these criteria is to produce kernels (and thus, TFRs) which will enable accurate classification without explicitly defining, a priori, the underlying structure that differentiates individual classes. These kernels, which are optimized to discriminate among multiple classes of signals, are referred to as signal class-dependent kernels, or simply class-dependent kernels. Until now, our technique has utilized the Rihaczek TFR as the base representation, deriving the optimal smoothing in time and frequency from this representation. Here the performance of the class-dependent approach is investigated in relation to the choice of the base representation. Classifier performance using several base TFRs is analyzed within the context of radar transmitter identification. It is shown that both the Rihaczek and the Wigner-Ville distributions yield equivalent results, far superior to the short-time Fourier transform. In addition, a correlation reduction step is presented. This improves performance and extensibility of the class-dependent approach