Study Of Robust Adaptive Beamforming Algorithm Based On The Maximum Expected Signal Power

With the development of array signal processing, adaptive beamforming technology has been widely used in modern wireless communication systems due to its excellent performance. The classical adaptive beamforming algorithms can achieve the goals of signal recovery, distortion compensation and interference suppression under the assumption of ideal conditions. However, in practical applications, due to the influence of external factors, such as multipath scattering, mutual coupling between array elements and calibration errors, the performance of these classical algorithms will be reduced sharply. With the study of adaptive beamforming theory, a robust adaptive beamforming algorithm based on the maximum expected signal power is proposed in this paper. The main contents are as follows:1. Based on the introduction of the array antennas’ history and the basic principle of the adaptive beamforming, three performance measurement criterias of multi antenna systems are discussed. The shortage of the two classical adaptive beamforming algorithms proves that it’s necessary to research the robust adaptive beamforming algorithms.2. With the introduction of the loaded sample matrix inversion algorithm and the algorithms based on the uncertainty set, this paper proposes a robust adaptive beamforming algorithm based on the maximum expected signal power. The algorithm can minimize the output power of the beamformer and maximize the expected signal power while the distortionless response is maintained. To solve the optimization model, we propose a low complexity algorithm based on the combination of alternating direction multiplication and the steepest gradient descent. The proposed algorithm overcomes the sensitivity of the parameters’ selection and has better output performance while the steering vector’s error is large. So the proposed algorithm has better robustness and adaptability.3. Several simulations in different mismatch scenarios of the proposed algorithm are presented. The simulation results show that the proposed algorithm has better robustness to all kinds of mismatch scenarios. Its performance is better than the loaded sample matrix inversion algorithm and the algorithms based on the uncertainty set, which has a higher output signal to interference and noise ratio.