| 基于改进粒子群算法的压缩感知 |
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| 引用本文： | 陈江琦,马尽文.基于改进粒子群算法的压缩感知[J].信号处理,2017,33(4):488-495. |
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| 作者姓名： | 陈江琦 马尽文 |
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| 作者单位： | 北京大学数学科学学院信息科学系和数学及其应用教育部重点实验室 |
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| 基金项目： | 国家自然科学基金（61171138）资助 |
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| 摘 要： | 在稀疏信号处理中，压缩感知能够用较低的采样频率对稀疏信号进行压缩采样，而信号重建的问题则可归结为一个最优化问题，并可采用粒子群算法进行求解。针对压缩感知问题，本文对传统的粒子群算法进行了深入的分析和改进，得到了粒子数目的下界，并提出了三维环形邻域结构和多群协作机制，依此建立了有效的感知压缩重建方法，且将其应用于二维稀疏信号的重建。最后，本文通过在模拟和真实数据上实验结果验证了这种新型感知压缩方法的有效性和优越性。
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| 关 键 词： | 信号采样 压缩感知 稀疏信号 粒子群算法 多群协作机制 |
| 收稿时间： | 2016-12-01 |
The Improved Particle Swarm Optimization Algorithm Based Compressive Sensing |
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| Affiliation: | Department of Information Science, School of Mathematical Sciences and LMAM, Peking University |
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| Abstract: | In the field of sparse signal processing, compressive sensing is able to make signal sampling at a lower sampling frequency, while its reconstruction problem is equivalent to a special optimization problem that can be solved by the particle swarm optimization algorithm. For the purpose of compressive sensing, we firstly improve the particle swarm optimization algorithm by getting a theoretic lower-bound for the swarm size and proposing a new type of typology structure for the neighborhood and a multi-group collaboration learning paradigm and then construct the improved particle swarm optimization algorithm based compressive sensing reconstruction method especially applying for 2-D sparse signals. Finally, it is demonstrated by the experimental results on synthetic and real-world datasets that this proposed compressive sensing reconstruction method is feasible and effective. |
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