| 引用本文: | 国强,赵莹.基于时反-压缩感知的浅海目标DOA估计算法[J].哈尔滨工业大学学报,2019,51(11):152.DOI:10.11918/j.issn.0367-6234.201810113 |
| GUO Qiang,ZHAO Ying.DOA estimation of shallow sea target based on time reversal and compressive sensing[J].Journal of Harbin Institute of Technology,2019,51(11):152.DOI:10.11918/j.issn.0367-6234.201810113 |
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| 摘要: |
| 浅海波导由于海底海面边界以及不均匀散射体的存在,使得其中声传播极其复杂,对信号处理造成了严重的干扰.本文针对浅海复杂环境下的多途效应对目标波达方向(Direction of Arrival, DOA)估计的不利影响,提出了一种基于时反-压缩感知(Time Reversal-Compressive Sensing, TR-CS)的目标DOA估计算法.该算法针对利用压缩感知(Compressive Sensing, CS)理论进行DOA估计时浅海多径对信道稀疏性的干扰,引入时间反转(Time Reversal, TR)理论对信号进行预处理,将时反处理后的信号再次送入信道,建立了基于时间反转理论的浅海目标DOA估计模型,利用时间反转理论的空时聚焦特性实现了对多途畸变的修正,并且以多径数目作为环境复杂度的度量,在不同复杂度环境下验证了该算法的有效性,最后分析了不同快拍数下该算法的性能.仿真结果表明:在低信噪比、小快拍的浅海条件下,时间反转理论的引入通过对接收信号的再次处理,虽然对计算量提出了要求,但是明显抑制了旁瓣杂波,提高了信道的稀疏性及阵列接收信号的信噪比,改善了复杂浅海环境下的DOA估计性能,并且在多途效应越为严重的环境中,性能提高越为明显. |
| 关键词: 波达方向估计 时间反转 压缩感知 多途效应 信道稀疏性 |
| DOI：10.11918/j.issn.0367-6234.201810113 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目: |
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| DOA estimation of shallow sea target based on time reversal and compressive sensing |
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GUO Qiang,ZHAO Ying
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(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China）
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| Abstract: |
| Shallow water waveguides are very complex in sound propagation due to the existence of seafloor boundary and inhomogeneous scatterers, which causes serious interference to signal processing. Aiming at the adverse effect of multipath on DOA estimation in complex shallow water environment, a DOA estimation algorithm based on time reversal-compressive sensing (TR-CS) is proposed in this paper. To solve the interference of shallow water multipath to channel sparsity when DOA estimation is performed by using compressive sensing (CS), the algorithm introduced the time reversal theory (TR) to preprocess the signal, transmitted the signal to the channel again, and established the DOA estimation model of shallow water target based on TR. The algorithm uses the space-time focusing property of TR to correct the multipath distortion and analyzes the performance of the algorithm under different snapshot numbers and complexity environments. Simulation results show that under low SNR and small snapshot conditions, the introduction of TR significantly suppressed sidelobe clutter, increased the sparsity of the channel and the signal-to-noise ratio of the array received signals, and improved the performance of DOA estimation in complex shallow water environment. The performance improvement was more obvious in the environment with more severe multipath effects. |
| Key words: DOA estimation time reversal compressive sensing multipath effect channel sparsity 〖FQ(+17mm。22,ZX-W〗收稿日期: 2018-10-22 作者简介: 国强(1972—),男,教授,博士生导师通信作者: 国强,guoqiang@hrbeu.edu.cn |
