A Robust Beamforming Algorithm with Steering Vector Mismatch

In order to effectively settle the issue of steering vector mismatch caused by non-ideal factors such as array element position disturbance and direction of arrival (DOA) estimation error, an improved chicken swarm optimization (ICSO) method is proposed to enhance the robustness of the beamforming algorithm. This method utilizes the characteristic of support vector machine (SVM) structural risk minimization, and combines it with the conventional diagonal loading (DL) beamforming algorithm. By selecting the appropriate loss function and penalty parameter, the ICSO method is finally employed to solve the standard support vector regression beam optimization model. As for the ICSO method, due to the particle swarm optimization (PSO) algorithm with fast convergence speed in the early stage of iteration, the firefly algorithm (FA) with novel attraction mechanism and the grey wolf optimization (GWO) algorithm with similar internal hierarchical order to the chicken swarm, these 3 algorithms are fully integrated into the conventional CSO algorithm. Moreover, the operation mechanism of removing duplicate solutions is introduced to increase the diversity of the population, thereby improving the optimization performance of the algorithm. The simulation results demonstrate that when the perturbation deviation of the array element position is 0.02λ, 0.20λ, and the DOA estimation error is 2°, the proposed method has better beam pointing and lower sidelobe level.