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2D off-grid DOA estimation using joint sparsity

机译：使用联合稀疏度的2D离网DOA估计

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摘要

Direction of arrival (DOA) estimation is an essential task in the array signal processing. In this study, the authors attempt to address the off-grid issue for the two-dimensional (2D) DOA estimation of a uniform rectangular array. To this end, they would offer a modelling for the 2D off-grid problem based on joint sparsity. Leveraging the block sparsity property, they propose an algorithm to jointly recover the DOAs as well as the off-grids. Moreover, they discuss that the smaller grid intervals would result in higher mutual correlation of the steering matrix columns which leads to the poor performance of the DOA estimation technique. On the other hand, large grid intervals would intensify the off-grid issue. Therefore, to establish a compromise, they suggest choosing a slightly large grid interval for the DOA estimation problem and solving the off-grid issue using the joint sparsity property. The simulation results confirm that the proposed method has better DOA estimation accuracy. A great advantage of the suggested DOA estimation scheme is that it is a single snapshot technique which does not require knowing the number of signal sources beforehand. Moreover, they have observed that the suggested scheme is more robust against noise and the large number of source signals.
机译：到达方向（DOA）估计是阵列信号处理中的基本任务。在这项研究中，作者尝试解决统一矩形阵列的二维（2D）DOA估计的离网问题。为此，他们将基于联合稀疏性为2D离网问题提供建模。利用块稀疏性，他们提出了一种算法来联合恢复DOA和离网。此外，他们讨论了较小的网格间隔将导致导引矩阵列的较高互相关性，从而导致DOA估计技术的性能较差。另一方面，较大的网格间隔会加剧离网问题。因此，要建立折衷方案，他们建议为DOA估计问题选择一个稍大的网格间隔，并使用联合稀疏属性来解决离网问题。仿真结果表明，该方法具有更好的DOA估计精度。建议的DOA估计方案的一大优势在于，它是一种单快照技术，不需要事先知道信号源的数量。而且，他们已经观察到所建议的方案对噪声和大量源信号具有更强的鲁棒性。

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