[01] Zhen Xiao-dan, School of Information Engineering, China University of Geosciences (Beijing), Beijing, China. [02] Luo Xuan-chi-cheng, School of Information Engineering, China University of Geosciences (Beijing), Beijing, China. [03] Li Mei, School of Information Engineering, China University of Geosciences (Beijing), Beijing, China.

In order to get a better display in correlation identification, there are two types of deconvolution algorithms applied in correlation identification theory, which includes traditional frequency domain deconvolution algorithm and Wiener filtering deconvolution algorithm. This article derived the derivation of Wiener filter deconvolution and solved the zero problem traditional frequency domain deconvolution problem, and gave out the frequency domain expression of the system impulse response of the Wiener deconvolution filter. Then, after the comparison of the two deconvolution algorithms, it was shown that the identification results of the Wiener filter deconvolution algorithm were better than traditional frequency domain deconvolution algorithm when using the m-sequence as the system’s input signal. Finally, the Wiener filtering deconvolution algorithm was applied into correlation identification theory and achieved good identification results.

Correlation Identification, M-Sequences, Traditional Frequency Domain Deconvolution Algorithm, Wiener Filtering Deconvolution Algorithm

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