Research and Improvement of Beamforming Algorithm for Low Signal-to-Noise Ratio and Low Sampling Points

When the power of the input signal is small and there are noise and interference components in the system, a low signal-to-noise ratio will occur, affecting the output of the entire system. Especially when beamforming is used in combination with eigen decomposition, the signal and the subspace obtained by matrix decomposition has errors, resulting in mismatch of the steering vectors obtained by such methods. In addition, the performance the beamformer will be seriously degraded due to the error of the sampled data and the components of the desired signal, the limited number of sampling points, and the disturbance of the array. Based on the method of steering vector estimation, this paper proposes a new beamforming algorithm, which can effectively solve the problem of steering vector mismatch caused by subspace errors. First, the algorithm estimates the sampling covariance matrix. Then, extracting the interference steering vector to reconstruct the interference plus noise covariance matrix in a discrete manner. Finally, using the maximum correlation coefficient to optimize the steering vector of the signal of interest. The simulation results show that the performance of the beamformer is better than that of most classical algorithms under the condition of low signal-to-noise ratio and low number of sampling points. When there is array perturbation, the beamformer has high robustness and strong practicability.