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《Radio Communications Technology》 2018-02
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Reconstruction Algorithm of Compressed Sensing Based on Principal Component Analysis

LI Liang;TIAN Wenbiao;Unit 91872,PLA;Signal and Information Processing Provincial Key Laboratory in Shandong,Naval Aeronautical University;  
Considering the contradiction between the actual signal sparsity and the compressed sensing(CS) frame requirements,the sparsity of signal is fully exploited on the base of principal component analysis(PCA),and the number of observations is reduced by using CS.The principal component subspace pursuit algorithm is proposed to recover the original signal accurately.Theoretical analysis and experiments show that CS based on PCA can fully exploit the signal sparsity and improve the precision of reconstruction.Under the premise of saving 90% of the sampling resources,the reconstructed result of the new method can still reach a reconstructed SNR level of 20 d B.
【Fund】: 国家自然科学基金项目(41476089 41606117 61671016);; “泰山学者”建设工程专项资助项目
【CateGory Index】: TN911.7
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