Sparsity-based DOA estimation using co-prime arrays

By Yimin D. Zhang, Moeness G. Amin, Braham Himed. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2013

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A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming (2017, background): “By conducting the equivalent virtual array signal, the available DOFs can be significantly increased through spatial smoothing [15], [27], sparse reconstruction [11], [13], compressive sensing [14], etc.”
Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation (2018, methodology): “To avoid the estimation performance loss, the sparse signal reconstruction algorithm [15] exploits all the non-uniform virtual array signals for DOA estimation, where the sparsity of the sources is considered.”
Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint (2013, methodology): “However, the application of spatial smoothing reduces the obtained virtual array aperture [6].”
Augmented Nested Arrays With Enhanced DOF and Reduced Mutual Coupling (2017, background): “Therefore, many array signal processing algorithms, such as beam-forming [9], [10], direction of arrival (DOA) estimation [9]–[12] and sparse DOA recovery [13]–[15], could be performed based on the virtual array (instead of the physical array).”
Generalized Coprime Array Configurations for Direction-of-Arrival Estimation (2015, methodology): “Such sparse signal reconstruction is achieved using the recently developed compressive sensing techniques [22], [23].”