MATLAB Implementation of Adaptive Beamforming with FFT Filter Bank

This implementation explores an adaptive beamformer that leverages an FFT filter bank structure. The system can operate with or without integrated Direction of Arrival (DOA) estimation and tracking functionality, representing the frequency-domain adaptation of the classical Frost beamformer architecture. The adaptive beamformer constitutes a sophisticated signal processing technique designed to enhance target signals while simultaneously suppressing interfering sources and background noise. By employing an FFT filter bank framework, the beamformer processes signals in the frequency domain, enabling more efficient computation and improved performance across various applications. Key implementation aspects include: - FFT-based filter bank decomposition for frequency-domain processing - Adaptive weight computation using LMS or RLS algorithms - Optional DOA estimation module using MUSIC or ESPRIT algorithms - Real-time tracking capability through recursive parameter updates - Frequency-bin specific beamforming weights application The MATLAB implementation typically involves functions for signal framing, FFT computation, covariance matrix estimation, and adaptive weight calculation. The core algorithm maintains phase alignment across frequency bins while optimizing signal-to-interference-plus-noise ratio (SINR) through constrained adaptation.