Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint

By Zhao Tan, Yonina C. Eldar, Arye Nehorai. IEEE Transactions on Signal Processing, 2013

299 citations recorded by the source corpus.

We consider the problem of direction of arrival (DOA) estimation using a recently proposed structure of nonuniform linear arrays, referred to as co-prime arrays. By exploiting the second order statistical information of the received signals, co-prime arrays exhibit O(MN) degrees of freedom with only M+N sensors. A sparsity-based recovery algorithm is proposed to fully utilize these degrees of freedom. The suggested method is based on the developing theory of super resolution, which considers a continuous range of possible sources instead of discretizing this range onto a grid. With this approach, off-grid effects inherent in traditional sparse recovery can be neglected, thus improving the accuracy of DOA estimation. We show that in the noiseless case it is theoretically possible to detect up to [MN/ 2] sources with only 2M+N sensors. The noise statistics of co-prime arrays are also analyzed to demonstrate the robustness of the proposed optimization scheme. A source number detection method is presented based on the spectrum reconstructed from the sparse method. By extensive numerical examples, we show the superiority of the suggested algorithm in terms of DOA estimation accuracy, degrees of freedom, and resolution ability over previous techniques, such as MUSIC with spatial smoothing and discrete sparse recovery.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/4ef142e25149150cb6a1e7c9b21d3d051925069c

How researchers see this paper

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In Signal Processing

1. P. Vaidyanathan
2. Jian Li
3. P. Stoica
4. Chun-Lin Liu
5. Lihua Xie
6. Zai Yang
7. Abdelrahman Taha
8. Ahmed Alkhateeb

Citations in the corpus

• Remarks on the Spatial Smoothing Step in Coarray MUSIC (2015, methodology): “It should be mentioned herein that when DOA estimation based Manuscript received January 16, 2015; revised February 24, 2015; accepted February 27, 2015.”
• Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019, background): “Therefore, it is interesting to explore the optimization of the active element selection, leveraging tools from nested arrays [37], co-prime arrays [38], [39], incoherence frames [40], and difference sets [33], [41].”
• Sparse Methods for Direction-of-Arrival Estimation (2016, methodology): “We will discuss three covariance-based gridless sparse methods: GLS in [136], the SMV-based atomic norm method in [147] and the low rank matrix denoising approach in [148].”