A Novel and Low Complexity ESPRIT Method

Bao Zhi-qiang; Wu Shun-jun; Zhang Lin-rang

doi:10.3724/SP.J.1146.2006.00051

Volume 29 Issue 9

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2007 > 29(9): 2042-2046

Bao Zhi-qiang, Wu Shun-jun, Zhang Lin-rang. A Novel and Low Complexity ESPRIT Method[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2042-2046. doi: 10.3724/SP.J.1146.2006.00051

Citation:

Bao Zhi-qiang, Wu Shun-jun, Zhang Lin-rang. A Novel and Low Complexity ESPRIT Method[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2042-2046. doi: 10.3724/SP.J.1146.2006.00051

Bao Zhi-qiang, Wu Shun-jun, Zhang Lin-rang. A Novel and Low Complexity ESPRIT Method[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2042-2046. doi: 10.3724/SP.J.1146.2006.00051

Citation:

Bao Zhi-qiang, Wu Shun-jun, Zhang Lin-rang. A Novel and Low Complexity ESPRIT Method[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2042-2046. doi: 10.3724/SP.J.1146.2006.00051

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A Novel and Low Complexity ESPRIT Method

doi: 10.3724/SP.J.1146.2006.00051

Received Date: 2006-01-11
Rev Recd Date: 2006-06-26
Publish Date: 2007-09-19

Abstract

Abstract

In this paper, a low complexity ESPRIT algorithm based on power method and QR decomposition is presented for direction finding, which doesnot require the priori knowledge of sources number and the predetermined threshold in separation of the signal and noise eigen-values. Firstly, the estimation of noise subspace is obtained by the power of covariance matrix and a novel source number detection method without eigen-decomposition is proposed based on QR decomposition. Furthermore, the eigen-vectors of signal subspace can be determined according to Q matrix, and then the directions of signals could be computed by the ESPRIT algorithm. In determining the source-number and subspace, the proposed algorithm has a substantial computational saving with the approximation performance compared with the Single-Vector-Decomposition (SVD) based algorithm. The simulation results demonstrate its effectiveness and robustness.

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References(1)

References

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