基于混合匹配追踪算法的MIMO雷达稀疏成像方法

王伟; 张斌; 李欣

doi:10.11999/JEIT151453

基于混合匹配追踪算法的MIMO雷达稀疏成像方法

doi: 10.11999/JEIT151453

王伟¹,
张斌¹,
李欣¹

基金项目:

国家自然科学基金(61571148)，中国博士后特别资助(2015T80328)，中国博士后科学基金(2014M550182)，黑龙江省博士后特别资助(LBH-TZ0410)，哈尔滨市科技创新人才专项(2013RFXXJ016)

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出版历程
- 收稿日期: 2015-12-22
- 修回日期: 2016-06-17
- 刊出日期: 2016-10-19

An Imaging Method for MIMO Radar Based on Hybrid Matching Pursuit

Funds:

The National Natural Science Foundation of China (61571148), China Postdoctoral Special Funding (2015T80328), China Postdoctoral Science Foundation (2014M550182), Heilongjiang Province Postdoctoral Special Fund (LBH-TZ0410), Innovation of Science, Technology Talents in Harbin (2013RFXXJ016)

摘要

摘要: 多输入多输出(MIMO)雷达作为一种新型的雷达体制，其成像兼具高分辨率与实时性的优点。由于观测区域的稀疏性，MIMO雷达成像可以用压缩感知的方法进行处理。而现有的MIMO雷达稀疏成像的贪婪恢复算法中，正交匹配追踪算法(OMP)存在成像图像有伪影的缺点，子空间追踪算法(SP)则受到低分辨率的困扰。针对上述问题，该文提出一种称为混合匹配追踪算法的压缩感知贪婪算法以实现MIMO雷达稀疏成像。通过将两种贪婪恢复算法结合起来，利用OMP 算法选择基信号的正交性和SP 算法具有基信号选择的回溯策略，来重构出高分辨率且没有伪影的雷达图像。仿真实验验证了所提算法的有效性。
- MIMO雷达 /
- 压缩感知 /
- 稀疏成像 /
- 贪婪算法
Abstract: MIMO radar is an emerging radar system that has significant potential. MIMO radar can provide high resolution and real-time imaging solution. Because of the sparsity of the observation zone, the task of MIMO radar imaging can be formulated as a problem of sparse signal recovery based on Compressed Sensing (CS). In MIMO radar imaging application based on CS, existing greedy algorithms, such as the Orthogonal Matching Pursuit (OMP) algorithm and the Subspace Pursuit (SP) algorithm, suffer from artifacts and low-resolution, respectively. To deal with the drawback of existing greedy algorithms, a Hybrid Matching Pursuit (HMP) algorithm is proposed to combine the strengths of OMP and SP. By using of the orthogonality among selected basis-signals and the backtracking strategy for basis-signal reevaluation, the HMP algorithm can reconstruct high-resolution radar image with no artifacts. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm.
- MIMO radar /
- Compressive sensing /
- Sparse imaging /
- Greedy algorithm

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参考文献(34)

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