Performance analysis on applications of LV distribution. - Luo, Shan. (2013)
Time-frequency analysis is widely used in revealing the joint time-frequency characteristics of non-stationary signals which have been used in many fields, such as sonar, wireless communication, radar target recognition and imaging. Many methods have been developed for processing these signals, such as the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), local polynomial periodogram (LPP), polynomial Fourier transform (PFT), fractional Fourier transform (FrFT), etc. Each of these methods has inherent merits and shortcomings, which lead to limited applications. For example, bilinear transforms, e.g., the WVD, provide high energy concentration but suffer from cross-terms. Linear transforms including STFT and PFT are free of cross-terms but can not achieve good signal concentration. Many efforts have been devoted to find methods having both high concentration and negligible cross-terms, usually by using various time-frequency windows, or kernels. However, there always exists a trade-off between auto-terms and cross-terms in kernel based distributions, which often limits further improvement on signal concentration in the time-frequency domain.