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Wang L.,Electronic Engineering Institute | Wang L.,Key Laboratory of Electronic Restricting Technique of Anhui Province | Cui C.,Electronic Engineering Institute | Cui C.,Key Laboratory of Electronic Restricting Technique of Anhui Province
Journal of Electronics | Year: 2012

A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method. © 2012 Science Press, Institute of Electronics, CAS and Springer-Verlag Berlin Heidelberg. Source


Wang L.-B.,Electronic Engineering Institute of PLA | Wang L.-B.,Key Laboratory of Electronic Restricting Technique of Anhui Province | Cui C.,Electronic Engineering Institute of PLA | Cui C.,Key Laboratory of Electronic Restricting Technique of Anhui Province
Yuhang Xuebao/Journal of Astronautics | Year: 2012

A novel method for two-dimensional direction-of-arrival estimation and automatically paired is proposed in a sparse reconstruction framework. First, two-dimensional sparse linear model is built based on the cross-correlation matrix of the signals received by two uniform linear arrays. Then this two-dimension model is transformed into simultaneous sparse reconstruction model by means of some mathematical manipulations. Second, the later model is solved, and coarse estimations of elevation and azimuth are obtained and embedded in the two-dimensional sparse linear model. Consequently, the number of active atoms is reduced and the computational burden is decreased. Moreover, the number of signal sources resolved by this novel approach and the computational complexity are analyzed in theory. Finally, the excellent estimation accuracy of the method is demonstrated through a series of computer simulations. Source

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