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Improvement of Direction of Arrival (DoA) Estimation using Compressed Sensing Based on Covariance Matrix

Hoda Zarghami; Mohammad Reza Moniri; Ramin Shaghaghi
In this paper, a new method for the direction of arrival (DoA) estimation using sparse representation of covariance matrix is proposed by using a non-uniform linear array. By vectoring of covariance matrix of non-uniform linear array, a new vector will be derived. This vector is similar to received vector of a virtual uniform linear array with a large number of antennas. As the covariance matrix of this vector is rank one, then the DoA of one source will be estimated. An approach to solve this problem is spatial smoothing technique. In this method, the obtained array is divided into multiple sub-arrays and the covariance matrix of each sub-array will be estimated. Using the average of sub-arrays covariance matrix, a new full rank covariance matrix will be obtained. By quantizing the continuous angle space into a discrete set, DoA estimation can be modeled as a compressed sensing problem. The DoA of sources will be estimated by minimization of the difference between obtained covariance matrix and its estimation. The estimated DoAs has an error due to quantization of continuous angle space. To improve the estimation accuracy, we propose a method. Simulation results show that the proposed method can improve the estimation accuracy.
Select Volume / Issues:
Year:
2021
Type of Publication:
Article
Keywords:
Direction of Arrival Estimation; Compressive Sensing; Covariance Matrix; Virtual Linear Array
Journal:
IJECCE
Volume:
12
Number:
2
Pages:
21-30
Month:
March
ISSN:
2249-071X
Hits: 23

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