计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
3期
208-211
,共4页
压缩感知%稀疏表示%子空间追踪算法%自适应%正则化
壓縮感知%稀疏錶示%子空間追蹤算法%自適應%正則化
압축감지%희소표시%자공간추종산법%자괄응%정칙화
compressed sensing%sparse representation%subspace pursuit algorithm%adaptation%regularization
针对压缩感知中未知稀疏度信号的重建问题，提出一种新的压缩感知的信号重建算法，即自适应正则化子空间追踪（Adaptive Regularized Subspace Pursuit，ARSP）算法，该算法将自适应思想、正则化思想与子空间追踪（Subspace Pursuit，SP）算法相结合，在未知信号稀疏度的情况下，自适应地选择支撑集原子的个数，利用正则化过程实现支撑集的二次筛选，最终能实现信号的精确重构。仿真结果表明，该算法能够精确重构原始信号，重建效果优于SP算法、正则化正交匹配追踪（ROMP）算法、稀疏度自适应匹配追踪（SAMP）算法、压缩采样匹配追踪（CoSaMP）算法等。
針對壓縮感知中未知稀疏度信號的重建問題，提齣一種新的壓縮感知的信號重建算法，即自適應正則化子空間追蹤（Adaptive Regularized Subspace Pursuit，ARSP）算法，該算法將自適應思想、正則化思想與子空間追蹤（Subspace Pursuit，SP）算法相結閤，在未知信號稀疏度的情況下，自適應地選擇支撐集原子的箇數，利用正則化過程實現支撐集的二次篩選，最終能實現信號的精確重構。倣真結果錶明，該算法能夠精確重構原始信號，重建效果優于SP算法、正則化正交匹配追蹤（ROMP）算法、稀疏度自適應匹配追蹤（SAMP）算法、壓縮採樣匹配追蹤（CoSaMP）算法等。
침대압축감지중미지희소도신호적중건문제，제출일충신적압축감지적신호중건산법，즉자괄응정칙화자공간추종（Adaptive Regularized Subspace Pursuit，ARSP）산법，해산법장자괄응사상、정칙화사상여자공간추종（Subspace Pursuit，SP）산법상결합，재미지신호희소도적정황하，자괄응지선택지탱집원자적개수，이용정칙화과정실현지탱집적이차사선，최종능실현신호적정학중구。방진결과표명，해산법능구정학중구원시신호，중건효과우우SP산법、정칙화정교필배추종（ROMP）산법、희소도자괄응필배추종（SAMP）산법、압축채양필배추종（CoSaMP）산법등。
Facing the problem of reconstruct signals with unknown sparsity in compressed sensing, this paper presents a new signal reconstruction algorithm, named Adaptive Regularized Subspace Pursuit(ARSP)algorithm. The proposed algo-rithm is associated with adaptive process and regularized process and Subspace Pursuit algorithm(SP). The new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process which realizes the second selecting of the atoms in the support set although the sparsity of the original signal is unknown. The simulation results show that the proposed algorithm can reconstruct the original signal accurately, and it outperforms SP algorithm, Regularized Orthogonal Matching Pursuit(ROMP)algorithm, Sparsity Adaptive Matching Pursuit algorithm (SAMP), Compressive Sampling Matching Pursuit(CoSaMP)algorithm.
