Cross-term Reduction in WVD with FrFT Denoising

In this paper, we propose a novel method to identify unknown chirp signals in low signal-to-noise(SNR) environments, and represent the signals with excellent clarity as a time frequency representation (TFR). The authors have proposed to evaluate the performance of this method based on the Fractional Fourier Transform (FrFT) denoising, for the mono- or multi component chirps, analyzed using the Wigner Villie Distribution (WVD). The method offers excellent rejection capability of cross-terms in the WVD and more robustness against additive white Gaussian noise with pronounced time-frequency resolution. The motivation behind the proposed scheme is the inherent ability of FrFT to process chirp signals. The approach works equally well with nonlinear chirps as well. Our simulation results indicate that this method outperforms the other available methods, especially at low SNRs. We have also done the performance analysis of this approach, in terms of SNR. We show that a very clean recovery of the time frequency distribution of the signals can be accomplished for SNRs as low as -11 dB.