Distributed MIMO radar using compressive sampling
Distributed MIMO radar using compressive sampling
复制标题
使用压缩采样的分布式 MIMO 雷达
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
H. Poor
中科院分区：
文献类型：
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作者：
A. Petropulu;Yaojiang Yu;H. Poor
考虑了一种分布式MIMO雷达，其中发射和接收天线属于小规模无线网络的节点。发射波形可以是不相关的，或者是相关的，以便实现期望的波束图案。在接收节点处采用压缩采样的概念以执行到达方向（DOA）估计。根据压缩采样理论，可以基于比奈奎斯特采样定理所需的少得多的样本来恢复在某些域中稀疏的信号。目标的波达方向在角度空间中形成稀疏向量，因此可以采用压缩采样进行波达方向估计。与其他方法相比，该方法在样本量少的情况下实现了MIMO雷达的上级分辨率。这在分布式场景中特别有用，其中每个接收节点处的结果需要被发送到融合中心。
A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at the receive nodes in order to perform direction of arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOAs of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center.