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DOA estimation of coherent and incoherent targets based on monostatic co-prime MIMO array

机译:基于单声道共序MIMO阵列的连贯和非连贯目标的DOA估计

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For the traditional DOA estimation of coherent and incoherent targets based on monostatic (massive) uniform dense array, the number of resolvable targets is limited to the number of physical sensors. To exceed the limitation, it is desirable to have sparse transmitting and receiving arrays. In this study, we consider the monostatic co-prime MIMO array with N sparse transmitters and 2M-1 sparse receivers in the DOA estimation of mixed coherent and incoherent targets. The sparse co-prime MIMO array with O(M + N) physical sensors generates a non-redundant and uniform sub-coarray with O(MN) contiguous sensors in the sum co-array. Based on the defined wide-sense or narrow-sense sum co-array equivalence, we can obtain different configurations of virtual MIMO arrays with O(MN) contiguous virtual sensors, and then construct the corresponding virtual data matrices, which provides different tradeoffs between the number of resolvable targets and the maximum number of mutually coherent targets that can be resolved. On the basis of the virtual data matrix and the conventional DOA estimation approaches such as MUSIC, O(MN) mixed coherent and incoherent targets can be resolved only with O(M + N) physical sensors, namely the number of resolvable targets exceeds the limitation of the number of physical sensors. Furthermore, the application of two additional operation frequencies extends the contiguous sub-coarray accompanied with the improvement of degree-of-freedom for more resolvable coherent and incoherent targets. Finally, simulation results demonstrate the effectiveness of the proposed DOA estimation method. (C) 2019 Elsevier Inc. All rights reserved.
机译:对于基于单体(巨大)均匀致密阵列的传统的DOA估计,可分辨目标的数量限制为物理传感器的数量。要超过限制,希望具有稀疏的发送和接收阵列。在这项研究中,我们考虑了具有N稀疏发射器的单声道共同原型MIMO阵列和混合相干靶的DOA估计中的N稀疏发射器和2M-1稀疏接收器。具有O(M + N)物理传感器的稀疏共继主要MIMO阵列,在总和共阵列中使用O(MN)连续传感器具有非冗余和均匀的子携带器。基于定义的广播或窄感应总结等效,我们可以使用O(MN)连续的虚拟传感器获得不同的虚拟MIMO阵列的配置,然后构造相应的虚拟数据矩阵,该虚拟数据矩阵提供不同的权衡可解析目标的数量和可以解决的相互相干目标的最大数量。在虚拟数据矩阵和传统的DOA估计方法(如音乐)的基础上,可以仅使用O(M + N)物理传感器来解析o(MN)混合的相干和非结合目标,即可解析目标的数量超过限制物理传感器的数量。此外,两种附加操作频率的应用延伸了随着更多可分辨的相干和不连贯的靶标的改善自由度的改善。最后,仿真结果证明了所提出的DOA估计方法的有效性。 (c)2019 Elsevier Inc.保留所有权利。

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