Fusion of time-frequency distributions and applications to radar signals

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. (C) 2006 SPIE and IS&T.