| 引用本文: | 徐慧峰,钱彦岭,温激鸿,等.基于粒子群优化的稀疏序列Bayes反卷积方法研究.[J].国防科技大学学报,2008,30(5):103-107.[点击复制] |
| XU Huifeng,QIAN Yanling,WEN Jihong,et al.Bayesian Deconvolution of Sparse Spike Trains by Particle Swarm Optimization[J].Journal of National University of Defense Technology,2008,30(5):103-107[点击复制] |
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| 基于粒子群优化的稀疏序列Bayes反卷积方法研究 |
| 徐慧峰, 钱彦岭, 温激鸿, 邱静 |
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(国防科学技术大学 机电工程与自动化学院，湖南 长沙 410073)
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| 摘要: |
| 在地下目标低频声波探测中，由于探测信号的混叠，难以判读反射目标的空间位置。应用信号处理方法求解时，目标信号是稀疏序列，求解方程是病态的。运用Bayes反卷积方法修正其病态性，并采用优化的粒子群算法求解，提高了系统的探测分辨率，同时降低了计算量。实际应用表明，该方法是有效的。 |
| 关键词: 稀疏序列 粒子群优化 Bayes反卷积 声学探测 |
| DOI： |
| 投稿日期：2008-02-18 |
| 基金项目:国家部委资助项目 |
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| Bayesian Deconvolution of Sparse Spike Trains by Particle Swarm Optimization |
| XU Huifeng, QIAN Yanling, WEN Jihong, QIU Jing |
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(College of Mechatronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073， China)
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| Abstract: |
| Because of the aliasing of the received waves and the sparse structure of the reflection signals, the temporal resolution is not good in acoustic detection of buried objects with low frequency waves. Thus, the problem of resolving the reflection position is notoriously ill-posed. In this paper, a maximum a posteriori estimator is presented to regularize the ill-posed problem, and an algorithm of particle swarm optimization is proposed to improve temporal resolution and reduce execution time. The results from research show that the method is efficient for using practical data in detecting objects buried in sand. |
| Keywords: sparse spike trains Particle Swarm Optimization(PSO) bayesian deconvolution acoustic detection |
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