Doa estimation using a sparse linear model based on eigenvectors

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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Authors

Corresponding author

Correspondence to Libin Wang.

Additional information

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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Key words

  • Direction-Of-Arrival (DOA) estimation
  • Sparse linear model
  • Eigen-value decomposition
  • Sparse solution finding

CLC index

  • TN911.23