| 引用本文: | 刘玉磊,梁俊,肖楠,等.粒子滤波匹配追踪重构算法.[J].国防科技大学学报,2017,39(5):108-114.[点击复制] |
| LIU Yulei,LIANG Jun,XIAO Nan,et al.Particle filtered matching pursuit for signal reconstruction[J].Journal of National University of Defense Technology,2017,39(5):108-114[点击复制] |
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| 粒子滤波匹配追踪重构算法 |
| 刘玉磊1, 梁俊1, 肖楠1, 胡猛1, 杨萌2 |
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(1. 空军工程大学 信息与导航学院， 陕西 西安 710077;2. 中国人民解放军94755部队， 福建 漳州 363000)
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| 摘要: |
| 针对现有贪婪迭代类压缩感知重构算法对非高斯量测噪声抵抗性差的问题，提出一种盲稀疏度下粒子滤波匹配追踪稀疏信号重构算法。该算法将鲁棒性更高的Huber损失函数替代常规的二次损失函数，用来增加对非高斯噪声的抵抗能力；并引入粒子滤波实现对原始信号的最优估计，以削弱量测噪声的影响；在信号稀疏度未知的条件下，结合稀疏度自适应匹配追踪算法实现盲稀疏度下的原信号重构。理论分析和仿真结果表明，所提算法可以有效抵抗因非高斯噪声干扰或稀疏度未知导致的重构精度降低，且重构性能优于现有典型贪婪迭代类算法。 |
| 关键词: 压缩感知 粒子滤波 非高斯噪声 盲稀疏度 重构 |
| DOI：10.11887/j.cn.201705018 |
| 投稿日期：2016-05-29 |
| 基金项目:国家自然科学基金资助项目（61501496）；陕西省自然科学基金资助项目（2012JM8004）；航空科学基金资助项目（2013ZC15008） |
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| Particle filtered matching pursuit for signal reconstruction |
| LIU Yulei1, LIANG Jun1, XIAO Nan1, HU Meng1, YANG Meng2 |
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(1. College of Information and Navigation, Air Force Engineering University, Xi′an 710077, China;2. The PLA Unit 94755, Zhangzhou 363000, China)
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| Abstract: |
| A particle filtered matching pursuit for compressive sensing of blind sparsity signal polluted by non-Gaussion noise was proposed, while the conventional detectors(e.g. least-squares estimates) were known to be sensitive to the non-Gaussion nature of noise. The proposed algorithm which combined the Huber cost(loss) function with an l1-norm did not need the sparse prior while it eliminated the interference of measuring noise by particle filter estimation. Meanwhile, sparsity adaptive matching pursuit was used to sift the effective support set so as to inverse the original states. Simulation results indicate that the proposed algorithm outperforms the existing greedy iterative inversions in the same condition, especially in the non-Gaussion noise situation. |
| Keywords: compressive sensing particle filter non-Gaussion noise blind sparsity reconstruction |
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