Continuous Ant Colony Optimization Based Weighted Subspace Fitting Fast Algorithm for DOA Estimation with Few Snapshots

Jiao  Ya-Meng; Huang  Jian-Guo; Han  Jing

doi:10.3724/SP.J.1146.2010.00783

Volume 33 Issue 4

May 2011

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Jiao Ya-Meng, Huang Jian-Guo, Han Jing. Continuous Ant Colony Optimization Based Weighted Subspace Fitting Fast Algorithm for DOA Estimation with Few Snapshots[J]. Journal of Electronics & Information Technology, 2011, 33(4): 972-976. doi: 10.3724/SP.J.1146.2010.00783

Citation:

Jiao Ya-Meng, Huang Jian-Guo, Han Jing. Continuous Ant Colony Optimization Based Weighted Subspace Fitting Fast Algorithm for DOA Estimation with Few Snapshots[J]. Journal of Electronics & Information Technology, 2011, 33(4): 972-976. doi: 10.3724/SP.J.1146.2010.00783

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Continuous Ant Colony Optimization Based Weighted Subspace Fitting Fast Algorithm for DOA Estimation with Few Snapshots

doi: 10.3724/SP.J.1146.2010.00783

Jiao Ya-Meng^{* 黄建国韩晶
,},
Huang Jian-Guo,
Han Jing

Received Date: 2010-07-28
Rev Recd Date: 2010-10-26
Publish Date: 2011-04-19

Abstract

Abstract

Weighted Subspace Fitting (WSF) algorithm is a well-known excellent algorithm for DOA estimation with low SNR and few snapshots. However, this algorithm is totally impractical for its prohibitive computational burden incurred by multi-dimensional nonlinear search. In order to solve this problem, Ant Colony Optimization (ACO) is introduced to combine with the WSF algorithm and a new algorithm with lower computational burden called ACO-WSF is proposed. The proposed algorithm exploits Gaussian kernel probability density function in the sampling process. The global maximum of WSF spatial spectrum function can be reached after reasonable iterations. Simulation results illustrate that the proposed algorithm not only provides similar performance as WSF algorithm and better performance than MUSIC algorithm in the situation of low SNR and few snapshots, but also reduces computational complexity significantly.
- Signal processing,
- DOA estimation,
- Weighted subspace fitting,
- Ant colony optimization,
- Computational complexity

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References(1)

References

Kim H and Viberg M. Two decades of array signal processing research[J].IEEE Signal Magazine.1996, 13(4):67-94[2]Li F and Lu Y. Unified bias analysis of subspace-based DOA estimation algorithms[J].In Multidimensional Signal Processing Algorithms and Application Techniques, Leondes，C T Ed. New York: Academic.1996, 77:149-192[3]Werner K and Jansson M. DOA estimation and detection in colored noise using addition noise-only data[J].IEEE Transactions on Signal Processing.2007, 55(11):5309-5322[4]Stoica P and Gershman A B. Maximum-likelihood DOA estimation by data-supported grid search[J].IEEE Signal Processing Letters.1999, 6(10):273-275[6]Zhou Yuren. Runtime analysis of an ant colony optimization algorithm for TSP Instance[J].IEEE Transactions on Evolutionary Computation.2009, 13(5):1083-1092[7]Juang Chia-feng and Hsu Chia-huang. Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control[J].IEEE Transactions on Industrial Electronics.2009, 56(10):3931-3940[8]Ferrandi F, Lanzi P L, Pilato C, Sciuto D, and Tumeo A. Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and System.2010, 29(6):911-924[9]Triay J and Cervello-Pastor C. An ant-based algorithm for distributed routing and wavelength assignment in dynamic optical networks[J].IEEE Journal on Selected Areas in Communications.2010, 28(4):542-552[10]Socha K and Dorigo M. Ant colony optimization for continuous domains[J].European Journal of Operational Research.2008, 185(3):1155-1173

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