Estimation and classification of FM signals using time frequency transforms

This work deals with electronic warfare: we have to process an intercepted radar-emitted signal assumed to be frequency-modulated (FM). A battery of parametric models being available, we face the two main problems: the parameter estimation, given a model, and the classification, given the battery. For the former, we propose the classical maximum likelihood estimator (MLE) and an original numerical scheme to reach it. For the latter, we construct a test based on the estimated variances cross-ratios. Both perform very efficiently with respect to (w.r.t.) the Cramer-Rao lower bound (CRLB) and the rate of correct classification, respectively, for low signal-to-noise ratios (SNRs).