Covariance matrix reconstruction with iterative mismatch approximation for robust adaptive beamforming

Abstract

The covariance matrix reconstruction based robust adaptive beamforming (RAB) methods overcome the performance degradation due to the imprecise knowledge of the steering vector and the covariance matrix. However, high complexity limits the application of them. In this paper, we proposed a new RAB method based on interference plus noise covariance (INC) matrix reconstruction and desired signal steering vector estimation. In this method, nominal interference steering vectors are estimated by the Capon spatial spectrum, as well as noise power. Subsequently, the iterative mismatch approximation algorithm based on maximizing the beamformer output power is proposed to estimate all the incident signal steering vectors and powers, and the INC matrix is reconstructed. Finally, the beamformer is determined by the estimated INC matrix and desired signal steering vector. Simulation results indicate that the proposed method obtains better performance than other existed methods at both the high signal to noise ratio (SNR) and the complexity.

KEYWORDS:

• Robust adaptive beamforming
• INC matrix
• steering vector and power estimation
• computational complexity

Acknowledgements

Y.D. proposed the RAB method, as well as validated it with simulation and performed the original draft preparation. S.Z. contributed to the review and editing. W.C. provided resources and supervision. All authors read and agreed to the published version of the manuscript

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [grant number 61661012], Natural Science Foundation of Guangxi [grant numbers 2019GXNSFFA245002, 2018GXNSFAA281190], and the Dean Project of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing [grant numbers GXKL06190118, GXKL06180110, CRKL150107].