An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising
College of Information and Communication Engineering, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
Depaprtment of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA
Author to whom correspondence should be addressed.
Academic Editors: Zhiguo Shi, Yujie Gu and Rongxing Lu
Received: 29 March 2017 / Revised: 4 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
Co-prime arrays can estimate the directions of arrival (DOAs) of
sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Guo, M.; Chen, T.; Wang, B. An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising. Sensors 2017, 17, 1140.
Guo M, Chen T, Wang B. An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising. Sensors. 2017; 17(5):1140.
Guo, Muran; Chen, Tao; Wang, Ben. 2017. "An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising." Sensors 17, no. 5: 1140.
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