A training-based approach to classification of unknown transients with unknown arrival time and Doppler shift

Abstract

We present a training-based approach for the classification of noisy unknown transient signals with arbitrary range and Doppler shift (time and frequency shifts). The method uses the magnitude-square of the signal ambiguity function to remove the unknown shifts. An ambiguity domain template is then generated from labeled training data (tens of observations), and classification is performed using an inner product. The method is tested on synthetic transient signals in Gaussian noise and performs as well as or better than another previously proposed time-frequency based method, and an energy detector.