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In practical array signal processing applications, the performance of DOA (direction-of-arrival) estimation methods is known to degrade severely in the presence of angular spread. In this paper, a new approach of estimating parameter via block sparse Bayesian learning is proposed for multiple incoherently distributed sources. Unlike traditional subspace based methods, the new technique makes use of...
In this paper, we propose a novel method employing subspace fitting principle for DOA estimation of noncircular signal based on the sparse representation technology. The proposed method combines the signal information contained in both the covariance and elliptic covariance matrix of the received data matrix. We use the eigenvalue decomposition of the extended covariance to obtain the signal eigenvectors,...
In this paper, a new estimator of coherently distributed source employing the sparse representation technology is proposed by utilizing subspace fitting principle. The proposed method uses the eigenvalue-decomposition method on the sample covariance matrix of the sensor array received data and obtains the signal eigenvectors. We represent the generalized steering vectors of coherently distributed...
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