Abstract
Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve sources with only sensors, and is a popular sparse array structure due to its closed-form expressions for array configuration and the reduction of the mutual coupling effect. However, because of the existence of holes in its coarray, the performance of subspace-based direction of arrival (DOA) estimation algorithms such as MUSIC and ESPRIT is limited. Several coarray interpolation approaches have been proposed to address this issue. In this paper, a novel DOA estimation approach via direct coarray interpolation is proposed. By using the direct coarray interpolation, the reshaping and spatial smoothing operations in coarray-based DOA estimation are not needed. Compared with existing approaches, the proposed approach can achieve a better accuracy with lower complexity. In addition, an improved angular resolution capability is obtained by using the proposed approach. Numerical simulations are conducted to validate the effectiveness of the proposed approach.
Highlights
Array signal processing, including beamforming and direction of arrival (DOA) estimation, is of great interest [1,2,3,4,5,6,7,8,9]
We first intuitively examine the performance of direct coarray interpolation by showing the multiple signal classification (MUSIC) spectrum
The estimation performance of the proposed approach is examined by calculating the root mean square error (RMSE) versus input signal-to-noise ratio (SNR) and the number of snapshots
Summary
Array signal processing, including beamforming and direction of arrival (DOA) estimation, is of great interest [1,2,3,4,5,6,7,8,9]. For coarray-based MUSIC [16], a reshaping operation is first applied to the covariance matrix to obtain the equivalent received data vector of the coarray. Sensors 2017, 17, 2149 rank-recovered covariance matrix can be utilized to perform the MUSIC algorithm. The rank of the covariance matrix is recovered, which means that MUSIC can be readily performed to estimate the DOAs. Note that the noise is suppressed via the proposed nuclear norm minimization problem. An accurate DOA estimation can be obtained with lower complexity, and the angular resolution is improved through the proposed approach. It is worth noting that the proposed approach is focused on obtaining the interpolated and denoised covariance matrix.
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