WEAK LFM SIGNAL DECTECTION BASED ON WAVELET TRANSFORM MODULUS MAXIMA DENOISING AND OTHER TECHNIQUES

摘要

A new method for detecting weak linear frequency modulated (LFM) pulse signals buriednin additive white Gaussian noise (AWGN) is presented in this paper. The method isnbased on the features of wavelet transform modulus maxima (WTMM) denoising andnauto-correlation filtering theory. Firstly, the frequency-domain information is extractednby auto-correlation matched filtering, and is used to deduce the optimal wavelet decompositionnscales. Secondly, let the signal modulus dominate on the biggest scale after thenoptimal scales decomposition, then keeping the signal modulus and removing the noisenmodulus at each scale are performed by utilizing the different propagation properties ofnsignal and noise wavelet modulus maxima across the scales. Finally, a reconstructed signalnis obtained from the reserved signal modulus with an improved signal-to-noise ration(SNR), and is used for time-domain information extraction. At the same time, waveletndenoising depends on selecting an optimum wavelet that matches well the shape of then313n314 B. Le, Z. Liu & T. Gunsignal. The cross correlation coefficients between signal and db wavelets are calculatednand the optimal wavelet to analysis the LFM signal is selected. Simulations show thatnthe method can extract time-frequency information of LFM signal when SNR ≤ −6dB.