摘要
Using the signal reconstruction algorithm, an improved multipath matching pursuit (MMP) algorithm with sparsity self-adaption is proposed to solve the problems of large calculated amounts and unknown signal sparsity in the tree-based MMP depth-first algorithm. This algorithm introduces adaptive thinking into the MMP algorithm, the number of support set atoms is adaptively selected, and the path with the largest atomic matching probability is selected through pruning technology. This makes for a more effective algorithmic search path and greatly reduces the amount of calculation. The algorithm improves the inner product matching criterion, which can more accurately and efficiently select the atoms that match the residual signal in the measurement matrix. In the iterative process, support set atom backtracking and the variable step size method are used to improve the reconstruction accuracy. Compared with the traditional MMP algorithm, the proposed algorithm has a shorter calculation time, greater reconstruction accuracy, and greater practical application value.
投稿的翻译标题 | An improved multipath matching pursuit algorithm with sparsity self-adaption |
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源语言 | 繁体中文 |
页(从-至) | 1611-1617 |
页数 | 7 |
期刊 | Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University |
卷 | 42 |
期 | 11 |
DOI | |
出版状态 | 已出版 - 5 11月 2021 |
关键词
- Backtracking
- Compressed sensing
- Inner product matching criterion
- Multipath matching pursuit (MMP)
- Pruning
- Signal reconstruction
- Sparsity self-adaptation
- Variable step size