Deep-Sparse Array Cognitive Radar
Deep-Sparse Array Cognitive Radar
复制标题
DOI：
10.1109/sampta45681.2019.9030833
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Ahmet M. Elbir;S. Mulleti;Regev Cohen;Rong Fu;Yonina C. Eldar
中科院分区：
文献类型：
--
作者：
Ahmet M. Elbir;S. Mulleti;Regev Cohen;Rong Fu;Yonina C. Eldar
在基于天线阵列的雷达应用中，通常希望从全阵列中选择最佳子阵列，以实现硬件成本和分辨率之间的平衡。此外，在认知雷达系统中，稀疏子阵的选择是基于目标场景在那一刻。最近，针对单目标场景提出了一种基于深度学习的天线选择技术。在本文中，我们将这种方法扩展到多个目标，并评估性能的国家的最先进的到达方向估计技术结合建议的天线选择方法。为了根据目标DOA最佳地选择子阵列，我们设计了一个卷积神经网络，它接受阵列协方差矩阵作为输入，并选择最佳的稀疏子阵列，使误差最小化。一旦获得最佳稀疏子阵，从所选择的天线的信号被用来估计DOA。我们提供了数值模拟，以验证所提出的认知阵列选择策略的性能。我们表明，所提出的方法优于随机稀疏天线选择，它导致更高的DOA估计精度6 dB。
In antenna array based radar applications, it is often desirable to choose an optimum subarray from a full array to achieve a balance between hardware cost and resolution. Moreover, in a cognitive radar system, the sparse subarrays are chosen based on the target scenario at that instant. Recently, a deep-learning based antenna selection technique was proposed for a single target scenario. In this paper, we extend this approach to multiple targets and assess the performance of state-of-the-art direction of arrival estimation techniques in conjunction with the proposed antenna selection method. To optimally choose the subarrays based on the target DOAs, we design a convolutional neural network which accepts the array covariance matrix as an input and selects the best sparse subarray that minimizes the error. Once the optimum sparse subarray is obtained, the signals from the selected antennas are used to estimate the DOAs. We provide numerical simulations to validate the performance of the proposed cognitive array selection strategy. We show that the proposed approach outperforms random sparse antenna selection and it leads to a higher DOA estimation accuracy by 6 dB.