LFM宽带雷达信号的盲压缩感知模型

doi:10.7527/S1000-6893.2014.0020

Abstract

Abstract:

A novel framework of sub-Nyquist sampling and reconstruction for linear frequency modulation (LFM) radar signals based on the theory of blind compressed sensing (BCS) is proposed. This mechanism takes LFM signals as a sparse linear combination under an unknown order p of fractional Fourier transform (FRFT) domain. Firstly, we make use of the scheme of delays correction dechirp and good energy concentration of LFM signal in proper FRFT domain to determine the optimal order, which meets the convergence conditions. Secondly, we construct discrete FRFT (DFRFT) basis dictionary according to the specific sparse FRFT domain dominated by p. To reconstruct the sources, group sparse reconstruction algorithms are chosen with less data storage and lower computational complexity. Finally, the results are provided to verify the uniqueness of the proposed framework, realizing the undersampling and reconstruction without the knowledge of priori sparse basis for LFM radar signals under the theory of BCS. The novel framework can bring a new solution concerned about the under-sampling and detection for LFM signals under the environment of non-collaboration.

Key words: compressed sensing, linear frequency modulated, fractional Fourier transform, under-sampling, blind compressed sensing

CLC Number:

V557
TP391

FANG Biao, HUANG Gaoming, GAO Jun. A Blind Compressed Sensing Model for Linear Frequency Modulated Wideband Radar Signals[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(8): 2261-2270.

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A Blind Compressed Sensing Model for Linear Frequency Modulated Wideband Radar Signals

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