| 摘要: |
| 针对现用于成像的MIMO山体滑坡雷达均匀线性阵列数目过多、数据处理复杂度高的问题，引入稀疏阵列时分地基MIMO雷达模型，提出一种基于逆傅里叶变换和混合匹配追踪算法的成像方法。首先通过对雷达回波信号作逆傅里叶变换实现距离向压缩，并进行近似相位补偿，然后采用一种基于时延补偿因子稀疏基的压缩感知算法实现方位向压缩。同时针对多目标成像的伪影点问题，方位向数据压缩引入子空间追踪算法和正交匹配追踪算法的结合算法重构出高分辨率且没有伪影的二维图像。根据真实的山体滑坡监测成像场景参数，通过数值仿真验证了该方法能够在低于传统均匀阵列的天线数目情况下实现目标高质量成像，且具有一定的抗噪性。 |
| 关键词: 时分MIMO雷达 稀疏阵列 逆傅里叶变换 压缩感知 混合匹配追踪 |
| DOI：DOI: 10.3969/j.issn.1672-2337.2019.01.001 |
| 分类号:TN957.52 |
| 基金项目:国家自然科学基金（No.61561010）； 广西自然科学基金（No.2017GXNSFAA198089）； 广西科学研究与技术开发计划项目； 广西教育厅科研立项项目（No.KY2015LX096） |
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| A Sparse Imaging Algorithm for Time-Division MIMO Landslide Radar |
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JIANG Liubing, YANG Zhongli, CHE Li
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1.School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China;2.School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
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| Abstract: |
| There exist some problems of large number of uniform linear arrays and high computational complexity in MIMO landslide radar imaging. To solve these problems, in this paper, a new method using an inverse fast Fourier transform and hybrid matching pursuit algorithm is proposed based on a time-division MIMO radar model with sparse array. Firstly, the radar echo signal needs completing range compression by inverse fast Fourier transform, and approximate phase compensation is applied to range-compression data. Then, the azimuth compression is completed by compressive sensing algorithm based on a sparse basis matrix constructed by time-delayed compensation factor. Furthermore, to eliminate the artifacts introduced by multi-target imaging, a new method for azimuth compression combing orthogonal matching pursuit algorithm and subspace pursuit algorithm is introduced to reconstruct the 2D radar images with high resolution and no artifacts. In order to verify the validity of our algorithm, the simulation scenario is based on real landslide monitoring imaging parameters. The results show that the proposed method can achieve high quality imaging even the number of antennas is less than the traditional uniform linear array. By the way, our method has a good anti-noise property. |
| Key words: time-division MIMO radar sparse array inverse fast Fourier transform compressive sensing hybrid matching pursuit |
