MATLAB Simulation of 1D and 2D Adaptive Digital Beamforming (DBF)

This project involves MATLAB simulations of both one-dimensional and two-dimensional adaptive digital beamforming (DBF) systems. The simulation employs adaptive algorithms such as Least Mean Squares (LMS) or Recursive Least Squares (RLS) to dynamically adjust beam direction and shape, enhancing signal focus while suppressing interference sources. Through MATLAB's phased array toolbox and custom algorithm implementations, we analyze key performance metrics including sidelobe suppression ratios and mainlobe width to evaluate how different adaptive algorithms impact beamforming effectiveness. The simulation code structure typically includes array geometry configuration, signal model generation, adaptive weight computation using matrix operations (e.g., inv(Rxx)*Rxd for MMSE solutions), and beam pattern visualization through polar plots or surface graphs. Additionally, we explore parameter optimization techniques for adaptive DBF systems, such as adjusting convergence factors and regularization parameters, to improve system performance and adaptability across various operational scenarios. These simulations provide valuable insights into the practical implementation and advantages of adaptive beamforming technology for applications in radar, wireless communications, and acoustic signal processing.