Abstract
To reduce high computational cost of existing Direction-Of-Arrival (DOA) estimation techniques within a sparse representation framework, a novel method with low computational complexity is proposed. Firstly, a sparse linear model constructed from the eigenvectors of covariance matrix of array received signals is built. Then based on the FOCal Underdetermined System Solver (FOCUSS) algorithm, a sparse solution finding algorithm to solve the model is developed. Compared with other state-of-the-art methods using a sparse representation, our approach also can resolve closely and highly correlated sources without a priori knowledge of the number of sources. However, our method has lower computational complexity and performs better in low Signal-to-Noise Ratio (SNR). Lastly, the performance of the proposed method is illustrated by computer simulations.
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Supported by the National Natural Science Foundation of China (No. 60502040) and the Innovation Foundation for Outstanding Postgraduates in the Electronic Engineering Institute of PLA (No. 2009YB005).
Communication author: Wang Libin, 1984, male, Ph. D. candidate.
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Wang, L., Cui, C. & Li, P. Doa estimation using a sparse linear model based on eigenvectors. J. Electron.(China) 28, 496–502 (2011). https://doi.org/10.1007/s11767-012-0764-4
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DOI: https://doi.org/10.1007/s11767-012-0764-4
Key words
- Direction-Of-Arrival (DOA) estimation
- Sparse linear model
- Eigen-value decomposition
- Sparse solution finding