| 一种改进的稀疏度自适应匹配追踪算法 |
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| 引用本文： | 王福驰,赵志刚,刘馨月,吕慧显,王国栋,解昊.一种改进的稀疏度自适应匹配追踪算法[J].计算机科学,2018,45(Z6):234-238. |
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| 作者姓名： | 王福驰 赵志刚 刘馨月 吕慧显 王国栋 解昊 |
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| 作者单位： | 青岛大学计算机科学技术学院 山东 青岛266071,青岛大学计算机科学技术学院 山东 青岛266071,青岛大学计算机科学技术学院 山东 青岛266071,青岛大学自动化与电气工程学院 山东 青岛266071,青岛大学计算机科学技术学院 山东 青岛266071,青岛大学计算机科学技术学院 山东 青岛266071 |
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| 基金项目： | 本文受“十二五”国家科技支撑计划(2014BAG03B05)资助 |
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| 摘 要： | 在信号稀疏度未知的情况下,稀疏度自适应匹配追踪算法(Sparsity Adaptive Matching Pursuit,SAMP)是一种广泛应用的压缩感知重构算法。为了优化SAMP算法的性能,提出了一种改进的稀疏度自适应匹配追踪(Improved Sparsity Adaptive Matching Pursuit,ISAMP)算法。该算法引入广义Dice系数匹配准则,能更准确地从测量矩阵中挑选与残差信号最匹配的原子,利用阈值方法选取预选集,并在迭代过程中采用指数变步长。实验结果表明,在相同的条件下,改进后的算法提高了重构质量和运算速度。
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| 关 键 词： | 压缩感知 匹配追踪 重构算法 Dice系数 |
Improved Sparsity Adaptive Matching Pursuit Algorithm |
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| WANG Fu-chi,ZHAO Zhi-gang,LIU Xin-yue,LV Hui-xian,WANG Guo-dong and XIE Hao.Improved Sparsity Adaptive Matching Pursuit Algorithm[J].Computer Science,2018,45(Z6):234-238. |
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| Authors: | WANG Fu-chi ZHAO Zhi-gang LIU Xin-yue LV Hui-xian WANG Guo-dong and XIE Hao |
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| Affiliation: | College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China,College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China,College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China,College of Automation and Electrical Engineering,Qingdao University,Qingdao,Shandong 266071,China,College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China and College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China |
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| Abstract: | Sparsity adaptive matching pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing under the condition that the sparsity is unknown.In order to optimize the performance of SAMP algorithm,an improved sparsity adaptive matching pursuit(ISAMP) algorithm was proposed.The proposed algorithm introduces generalized Dice coefficient for matching criterion,which improves its performance in selecting the most matching atom from measurement matrix for residual signal.Meanwhile,it uses threshold method to select preliminary set and adopts exponential variable step during the iteration.Experimental results show that the proposed algorithm improves reconstruction quality and computational time. |
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| Keywords: | Compressive sensing Matching pursuit Reconstruction algorithm Dice coefficient |
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