ROBUST ADAPTIVE BEAMFORMING BASED ON INTERFERENCE STEERING VECTOR ESTIMATION AND PROBABILITY CONSTRAINED UNDER POSITION ERRORS

Adaptive beamforming plays a vital role in interference suppression. However, when sensor position errors are present, the output performance of adaptive beamformers degrades significantly, particularly if the desired signal component appears in the training data. To tackle this challenge, a robust adaptive beamforming method based on interference steering vector estimation and probability constrained is proposed. Firstly, the Capon spectrum is used to estimate the number of signal sources and the corresponding inaccurate directions of arrival (DOA). Then, the steering vector and the power of each interference are estimated based on the robust Capon beamforming principle, while the average value of the small eigenvalues of sampling covariance matrix (SCM) is used as the noise power estimation, which can be used to reconstruct the interference-plus-noise covariance matrix. Finally, the probability constrained method is utilized to maximize the power of the desired signal while suppressing interferences and noise, followed by the optimization of the weight vector. Numerical simulations illustrate that the proposed method can effectively improve the robustness of the beamformer against sensor position errors, and achieve higher output performance than the comparison methods.