Fusion of time-frequency distributions and applications to radar signals

Abstract

We describe a new fusion method for time-frequency distribution (TFD) that increases the ability to detect and classify time-varying signals while suppressing signal-dependent artifacts and noise. This is achieved by applying a least-squares algorithm to estimate the second-order approximation Volterra series coefficients of the outputs of selected TFDs. These coefficients are used for the fusion of the selected TFDs and generate a new TFD. The proposed fusion method is compared with four other fusion methods in terms of resolution and signal-to-noise ratio (SNR) in the time-frequency (TF) plane. Five representative TFDs are fused to generate a new TFD and their performances are analyzed. The results show that the new fusion method considerably increases sharpness (resolution) and strength (SNR) of the signal in the TF plane and, furthermore, achieves better signal description over other fusion methods and the traditional TFDs.