A Robust Capon Beamforming Algorithm with Desired Signal Steering Vector Correction.

Area of Science:

• Signal Processing
• Array Signal Processing
• Adaptive Filtering

Background:

• Conventional Capon beamforming offers high gain and interference suppression but suffers performance degradation due to steering vector mismatch.
• Steering vector mismatch is a critical limitation in adaptive beamforming algorithms, impacting signal-to-interference-plus-noise ratio (SINR).

Purpose of the Study:

• To introduce a refined robust Capon beamforming algorithm that mitigates performance degradation caused by steering vector mismatch.
• To enhance the robustness and beam pattern performance of Capon beamforming in complex signal environments.

Main Methods:

• Leveraging the orthogonality between the steering vector and the noise space to correct the estimated expected signal steering vector.
• Optimizing the predicted steering vector of the desired signal to address mismatch issues.
• Correcting the covariance matrix using a desired signal elimination method to prevent signal self-cancelation.

Main Results:

• The proposed robust Capon beamforming algorithm demonstrates superior robustness compared to existing methods.
• Achieves a higher output signal-to-interference-plus-noise ratio (SINR) even with steering vector mismatch.
• Exhibits excellent beam pattern performance and insensitivity to steering vector mismatches in simulations.

Conclusions:

• The developed robust Capon beamforming technique effectively overcomes the limitations of conventional methods.
• The algorithm maintains high robustness in complex environments and with varying input signals.
• Offers a significant improvement in SINR and robustness for practical signal processing applications.