An Adaptive Compact Kernel TFD Analysis Based on Parameters Pre-estimation for the Multi-component Non-stationary LFM Signals

For the analysis of multi-component non-stationary linear frequency modulation (LFM) signals based on the high-resolution time-frequency distribution (TFD), an adaptive compact kernel-based TFD (ACKD) is presented in this paper. In the proposed ACKD, the signal is firstly represented in the ambiguity domain and then convolved with an optimal signal-dependent adaptive compact kernel. This kernel is designed based on the pre-estimation parameters which is related with the energy distribution directions and lengths of the auto-terms in the ambiguity domain. The Boashash-Sucic normalized instantaneous resolution performance measure method is used to evaluate and make comparisons. Simulation results are displayed to justify the feasibility and validity of the proposed method. In all presented cases, compared with the Wigner-Ville distribution (WVD) and the classical CKD, the proposed ACKD shows a significant performance improvement in terms of the cross-interferences rejection and energy concentration of component signals around their respective instantaneous frequency (IF).