本文選題：壓縮感知 + 塊稀疏信號　； 參考：《信號處理》2014年06期

【摘要】：壓縮感知,通過測量矩陣將原始信號從高維空間投影到低維空間,然后求解優(yōu)化問題,從少量投影中重構(gòu)出原始信號,是一種有效的信號采集技術(shù)。塊稀疏信號是具有特殊結(jié)構(gòu)的稀疏信號,其非零值是成塊出現(xiàn)的。針對該信號的特點(diǎn),提出一種采用正交多項(xiàng)匹配的塊稀疏信號重構(gòu)算法。該算法每次迭代選擇多個最大相關(guān)子塊,然后更新塊索引集,以及迭代余量,最后求廣義逆運(yùn)算重構(gòu)出原始信號。仿真結(jié)果表明,相比于大多數(shù)的現(xiàn)有算法,本文算法重構(gòu)成功率較高,運(yùn)行時間較短,復(fù)雜度較低。
[Abstract]:Compressed sensing, the original signal is projected from high-dimensional space to low-dimensional space by measuring matrix, then the optimization problem is solved, and the original signal is reconstructed from a small amount of projection. It is an effective signal acquisition technology.Block sparse signal is a sparse signal with special structure.According to the characteristics of the signal, a block sparse signal reconstruction algorithm using orthogonal multi-term matching is proposed.The algorithm selects multiple maximally correlated subblocks iteratively, then updates the block index set, and iterates the residue, and finally reconstructs the original signal by the generalized inverse operation.Simulation results show that compared with most existing algorithms, this algorithm has higher success rate, shorter running time and lower complexity.
【作者單位】： 南京郵電大學(xué)通信與信息工程學(xué)院;
【基金】：江蘇省自然科學(xué)基金(BK2011789) 東南大學(xué)毫米波國家重點(diǎn)實(shí)驗(yàn)室開放課題(K201318)資助課題
【分類號】：TN911.7

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