Echo processing of broad-band radar based on Bayesian compressive sensing
Wu Qiong, Bai Yechao, Zhang Xinggan
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(School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China)
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Published
2015-07-07
Issue Date
2015-07-07
Abstract
High sampling rate, huge storage pressure and long processing time are the important problems in the processing of broadband radar echo. Compressive sensing (CS) theory makes it possible to realize signal reconstruction accurately by using few measurement data with much smaller Nyquist sampling rate, which eases huge memory pressure in the broadband radar signal processing. This paper realizes the compressive sampling of radar echoes under the Bayesian compressive sensing (BCS) to reduce the data amount with the full target information restored. Based on the relevance vector machine (RVM), the fast marginal likelihood maximization algorithm improves the efficiency of target signal reconstruction. Simulation experiments show that when compared to CS, BCS reconstructs target information more precisely with relative error in time domain reduced by 10% and better noise resisting ability with 5 dB reduction of signal-to-noise ratio. Echo signal processing based on bayesian method can realize the adaptive observation of target echo, which further reduces the echo signal sampling rate
Wu Qiong, Bai Yechao, Zhang Xinggan.
Echo processing of broad-band radar based on Bayesian compressive sensing[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(4): 665-672
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References
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