Chengwei Zhou

Source Estimation Using Coprime Array: A Sparse Reconstruction Perspective

Direction-of-arrival (DOA), power, and achievable degrees-of-freedom (DOFs) are fundamental parameters for source estimation. In this paper, we propose a novel sparse reconstruction-based source estimation algorithm by using a coprime array. Specifically, a difference coarray is derived from a coprime array as the foundation for increasing the number of DOFs, and a virtual uniform linear subarray covariance matrix sparse reconstruction-based optimization problem is formulated for DOA estimation. Meanwhile, a modified sliding window scheme is devised to remove the spurious peaks from the reconstructed sparse spatial spectrum, and the power estimation is enhanced through a least squares problem. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOA estimation and power estimation as well as the achievable DOFs.

DECOM: DOA estimation with combined MUSIC for coprime array

In this paper, we propose a direction-of-arrival (DOA) estimation method by combining multiple signal classification (MUSIC) of two decomposed linear arrays for the corresponding coprime array signal processing. The title “DECOM” means that, first, the nonlinear coprime array needs to be DECOMposed into two linear arrays, and second, Doa Estimation is obtained by COmbining the MUSIC results of the linear arrays, where the existence and uniqueness of the solution are proved. To reduce the computational complexity of DECOM, we design a two-phase adaptive spectrum search scheme, which includes a coarse spectrum search phase and then a fine spectrum search phase. Extensive simulations have been conducted and the results show that the DECOM can achieve accurate DOA estimation under different SNR conditions.

Compressive Sensing Based Coprime Array Direction-of-Arrival Estimation

A coprime array has a larger array aperture as well as increased degrees-of-freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low-computational complexity. In this study, the authors focus on the problem of efficient direction-of-arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower-dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high-resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

Doa estimation by covariance matrix sparse reconstruction of coprime array

In this paper, we propose a direction-of-arrival estimation method by covariance matrix sparse reconstruction of coprime array. Specifically, source locations are estimated by solving a newly formulated convex optimization problem, where the difference between the spatially smoothed covariance matrix and the sparsely reconstructed one is minimized. Then, a sliding window scheme is designed for source enumeration. Finally, the power of each source is re-estimated as a least squares problem. Compared with existing methods, the proposed method achieves more accurate source localization and power estimation performance with full utilization of increased degrees of freedom provided by coprime array.

Robust adaptive beamforming based on DOA support using decomposed coprime subarrays

In this paper, we propose a novel robust adaptive beamforming algorithm with direction-of-arrival (DOA) support for the coprime array. Specifically, by using the property of coprime number, we may estimate the DOAs of sources by matching two super-resolution spatial spectra of the pair of decomposed coprime subarrays. After that, the power of each source can be estimated via a covariance matrix joint estimation problem corresponding to the pair of decomposed coprime sub-arrays. Taking the estimated DOAs and their corresponding power as the support information, the interference-plus-noise covariance matrix for the coprime array can be reconstructed, from which the minimum variance distortionless response beamformer weight vector can be calculated. Simulation results show that the proposed adaptive beamforming algorithm is more robust to signal look direction mismatch than the existing algorithms.

Coprime array adaptive beamforming based on compressive sensing virtual array signal

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

Toeplitz Matrix Reconstruction of Interpolated Coprime Virtual Array for DOA Estimation

A coprime array enables an increased number of degrees-of-freedom by deriving a non-uniform virtual array. However, existing work such as spatial smoothing fails to utilize all of the information provided by the coprime array, which results in performance loss. In this paper, we propose a novel coprime virtual array interpolation-based direction- of-arrival (DOA) estimation algorithm by Toeplitz matrix reconstruction. After investigating the challenges caused by the non-uniformity, we introduce the idea of array interpolation to construct a uniform linear virtual array, such that the information contained in the coprime virtual array can be fully utilized. According to the statistics of non-uniform coprime virtual array signal, we formulate a convex optimization problem for DOA estimation by reconstructing the covariance matrix of the equivalent received signals of the interpolated coprime virtual array under the Toeplitz constraint. Simulation results demonstrate the effectiveness of the proposed algorithm.