Ant Colony Clustering Algorithm for Underdetermined BSS

HE Xuansen; WANG Fang; CHAI Wenbiao; WU Liangmin

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 319-324

HE Xuansen, WANG Fang, CHAI Wenbiao, et al., “Ant Colony Clustering Algorithm for Underdetermined BSS,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 319-324, 2013,

Citation:

HE Xuansen, WANG Fang, CHAI Wenbiao, et al., “Ant Colony Clustering Algorithm for Underdetermined BSS,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 319-324, 2013,

HE Xuansen, WANG Fang, CHAI Wenbiao, et al., “Ant Colony Clustering Algorithm for Underdetermined BSS,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 319-324, 2013,

Citation:

HE Xuansen, WANG Fang, CHAI Wenbiao, et al., “Ant Colony Clustering Algorithm for Underdetermined BSS,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 319-324, 2013,

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Ant Colony Clustering Algorithm for Underdetermined BSS

College of Information Science and Engineering, Hunan University, Changsha 410082, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61072122) and the Key Project of Hunan Provincial Natural Science Foundation (No.11JJ20153).

Received Date: 2011-12-01
Rev Recd Date: 2012-03-01
Publish Date: 2013-04-25

Abstract

Abstract

In underdetermined blind source separation, in order to quickly and accurately estimate the mixing matrix and the source signals, this paper presents a new algorithm based on ant colony clustering. The basic ideal of the algorithm is that we utilize the linear clustering characteristic of sparse signals to estimate the number of sources and the column vector of the mixing matrix by estimating the directions of the straight lines. In the preprocessing step, the observed signals in time domain are transformed to sparse signals in frequency domain. Through normalizing the observed data the linearity clustering is translated to compact clustering, and then using ant colony clustering to get the number of source signals and the mixing matrix. Finally, based on the estimation of the mixing matrix, the source signals are recovered by linear programming method. The simulation results illustrate the availability and accuracy of the proposed algorithm.
- Blind source separation,
- Underdetermined blind source separation,
- Sparse representation,
- Ant colony clustering,
- Compact clustering

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References(13)

References

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