Diagonal Loading Beamforming Based on Aquila Optimizer

Traditional beamforming algorithms are only applicable to ideal environments. When the array antenna receives data under circumstances of small snapshots or large signal-to-noise ratio(SNR), noise eigenvalues of classic sample matrix inversion(SMI) and other algorithms will diverge, resulting in beam performance reduction. Therefore, a diagonal loading beamforming algorithm based on aquila optimizer is proposed in this paper. First, the identity matrix and the original covariance matrix are linearly combined, then aquila optimizer is used to optimize the process of the matrix diagonal loading, finally, the obtained improved loading value is combined with SMI to form the beam. Simulation results show that the proposed algorithm can improve the beam distortion caused by divergence of small eigenvalues, and performs well in high SNR, low SNR or small snapshots. Meanwhile, it has better stability and adaptability compared with SMI and traditional diagonal loading algorithm.