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The maximum amplitude of matching Fourier transform (MFT) spectrum based on re-sampling of sine (single pulse, SP) signal reflected the frequency of the signal. The analysis of the MFT spectrum of SP signal reflected the MFT theories, from which a method to estimate the frequency of SP signal was proposed; The similarity of the MFT spectrum of the SP and linear FM (LFM) signal was analyzed, then the parameter estimation of LFM signal was presented; The characteristic of promoting the MFT spectrum after adding windows was illustrated, so a method to improve the precision of estimation parameter by adding windows was proposed. The experiments and simulations show the efficiency of the parameters estimation of LFM signal.

The support vector clustering (SVC) algorithm was introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multi-component linear FM (LFM) signal, and to finish the detection of the components of the LFM signal. Meanwhile, the preprocessing to reduce the points' number of the input data-set for SVC, was proposed to improve the computation efficiency. And a novel cluster labeling method was developed to improve the SVC algorithm. The simulation results depict that the SVC-Radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-component LFM signal with low SNR

Based on the principle of correlation detection, a new algorithm of recognizing the binary-phase coded pulse compression radar signal is proposed, which is simple to realize. The simulation shows that the method can effectively complete the BPSK signal parameters estimation, and is superior to that using wavelet, under low signal-to-noise rate (SNR).

The support vector clustering (SVC) algorithm was introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multicomponent linear FM (LFM) signal, and to fulfil the detection of the components of the LFM signal. Meanwhile, an approach called near zero mean, for reducing the point number of the input data-set for SVC, was proposed to improve the computation efficiency. And a novel cluster labeling method was developed to improve the SVC algorithm. The simulation results depict that the SVC-radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-components LFM signal