Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm

WANG Hao; MA Qiming

doi:10.11999/JEIT170236

Volume 39 Issue 11

Nov. 2017

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WANG Hao, MA Qiming. Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2620-2626. doi: 10.11999/JEIT170236

Citation:

WANG Hao, MA Qiming. Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2620-2626. doi: 10.11999/JEIT170236

WANG Hao, MA Qiming. Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2620-2626. doi: 10.11999/JEIT170236

Citation:

WANG Hao, MA Qiming. Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2620-2626. doi: 10.11999/JEIT170236

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Modified Dominant Mode Rejection Robust Adaptive Beamforming Algorithm

doi: 10.11999/JEIT170236

WANG Hao¹,
MA Qiming¹

Received Date: 2017-03-20
Rev Recd Date: 2017-05-31
Publish Date: 2017-11-19

Abstract

Abstract

In order to solve the problem of adaptive beamformer performance degradation due to few snapshots and mainlobe angle mismatch, a robust dominant mode rejection adaptive beamforming algorithm based on modified covariance matrix is proposed. The algorithm employs modified covariance matrix to estimate dominant mode component and reconstructs covariance matrix. Then, the signal subspace is obtained by projecting the steering vector of the desired signal onto eigenvector of the reconstructed covariance matrix, for enhancing the performance of algorithm at low SNR, the algorithm uses the principal eigenvector of a positive define matrix which integrates the desired signal region to modify signal subspace, thus calibrated steering vector of the desired signal is obtained by projecting the presumed steering vector onto the modified signal subspace. Finally, the optimal weight is obtained from the reconstructed covariance matrix and the calibrated steering vector. Theoretical analysis and simulation show the effectiveness and robustness of the proposed algorithm.
- Signal and information processing,
- Adaptive beamforming,
- Dominant mode rejection,
- Modified covariance matrix,
- Few snapshots,
- Angle mismatch

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References(19)

References

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