A Robust Capon Beamforming Algorithm with Desired Signal Steering Vector Correction

The conventional Capon beamforming algorithm can achieve a high gain in the direction of desired signals and zero-trapping in the direction of interfering signals, providing a high output signal-to-interference-plus-noise ratio (SINR). However, when the steering vector of the desired signal is mismatched, the performance of the Capon beamforming algorithm degrades. In addressing this challenge, the present research introduces a refined algorithm. The core of the proposed robust Capon beamforming technique lies in leveraging the orthogonality between the steering vector and the noise space, the estimated expected signal steering vector is corrected. Based on this feature, the proposed algorithm meticulously optimizes the predicted steering vector of the desired signal, which can mitigate the problem of performance degradation caused by the mismatch in the steering vector. Moreover, the covariance matrix is corrected using the desired signal elimination method, which can overcome the problem of signal self-cancelation. Furthermore, through the optimization process, the proposed algorithm can maintain high robustness in complex environments and under the condition of different input signals, its beam pattern performance is more excellent. The results of simulation experiments show that the proposed algorithm demonstrates greater robustness compared to the currently available algorithms, can achieve a higher output SINR, and is insensitive to steering vector mismatch.