Research On The Time-Frequency Analysis And Parameters Estimation For Multi-Component Chirp Signals And Its Applications

Chirp signal is a typical non-stationary signal, which widely appears in systems such as communication, radar, sonar, biomedicine and earthquake exploring system and also be used as signal model for a lot of natural phenomena. The detection, the parameters estimation and the time-frequency representation for the multi-component Chirps were researched in this dissertation, using the reassignment time-frequency distribution-Hough transform (RTFD-HT), fractional Fourier transform (FRFT), and tree-parameter Chirp atom decomposition methods.The time-frequency distribution performance of ambiguity function, WVD, and Cohen class time-frequency distribution for the multi-component Chirps was described from the time-frequency concentration, the cross terms reduction, and the discriminability. Though WVD and ambiguity function is a better time-frequency representation for mono-component Chirp, the interferences of cross terms of the bilinear time-frequency distributions occurred for the multi-component Chirps. Using kernel function smooth can restrain the cross terms, but it results in a deterioration of the time-frequency concentration and the discriminability. Therefore, the Cohen class bilinear time-frequency distributions are not the best as a time-frequency analysis tool for the multi-component Chirps.Because the presence of the multi-component Chirps is barely identified directly from time-frequency distribution due to the existence of the cross terms and noise, the method of the detection and the parameters estimation for the multi-component Chirps was proposed, based on time-frequency distribution-Hough transform, which regards the bilinear time-frequency distribution of the signal as an image, applies the principle of the detecting line of Hough transform and finds the local maximal and relevant coordinate in order to detect the line and estimate its parameter. Confronting with the contradiction between restraining the cross terms and increasing the time-frequency concentration, we brought forward the improved methods of the reassignment time-frequency -HT and the image ridges extraction-HT. The simulation results have shown that the proposed method can not only eliminate false detection resulted from pretense peak value of the cross-term and shorten the time of Hough transform, but also increase the accuracy of the detection and parameters estimation and reduce noisy interference. Also the proposed method has been applied to detect a multi-target radar signal in noise and has, through simulation...