Adaptive Beamforming with Sample Matrix Inverse Algorithm Implementation

In adaptive beamforming, the Sample Matrix Inverse (SMI) algorithm represents a fundamental concept for determining optimal weight vectors. The SMI method computes the adaptive weights by inverting the sample covariance matrix of the received signals, providing faster convergence compared to iterative techniques. This implementation focuses on narrowband adaptive beamforming, making it particularly suitable for educational purposes and beginner-level understanding. The code demonstrates key MATLAB functions including covariance matrix estimation using sample matrix inversion, weight vector calculation through matrix operations, and beam pattern visualization. For developers seeking deeper insights into adaptive beamforming, this serves as an excellent resource with practical implementation examples. The SMI methodology finds extensive applications across various fields including radar systems, antenna array processing, and wireless communications, offering robust interference rejection capabilities through optimal signal combining techniques.