Digital Beamforming (BF) for Array Signal Processing

Digital Beamforming (DBF) is a core technology in array signal processing, designed to directionally enhance or suppress signals from specific directions. Compared to traditional analog beamforming, DBF employs digital signal processing to flexibly adjust beam direction and shape, making it widely applicable in radar, communication, and acoustic systems. In MATLAB implementations, DBF typically involves calculating complex weights for each antenna element based on the desired steering direction.

Classic DBF algorithms are generally based on phase weighting principles. By adjusting the signal phase at each antenna in the array, signals from the target direction are combined in-phase, while signals from other directions cancel each other out. This method offers moderate computational complexity and straightforward implementation, making it suitable for scenarios with high real-time requirements. For beginners, starting with uniform linear array beamforming helps gradually understand core concepts such as steering vectors and beam patterns. Code implementation often involves constructing a steering vector using array geometry and wavelength parameters.

FFT-based multi-beamforming algorithms utilize Fast Fourier Transform to efficiently generate multiple beams. By treating array received signals as spatial sampling points, FFT can simultaneously compute beam responses in multiple directions, significantly improving computational efficiency. This algorithm is suitable for systems requiring simultaneous monitoring of multiple directions, such as multi-user communications or wide-area surveillance. However, the fixed directional characteristics of FFT beams may limit flexibility, often requiring interpolation or post-processing optimization. In practice, this can be implemented using MATLAB's fft function with proper windowing to control sidelobe levels.

For engineering implementation, attention must be paid to the impact of array geometry (uniform/sparse arrangements), trade-offs between sampling rate and beam resolution, and anti-interference design (such as adaptive nulling). Comparing beam directivity and sidelobe levels of different algorithms through simulation provides intuitive understanding of performance differences. Simulation approaches typically involve modeling array response patterns and testing algorithms under various signal-to-noise conditions.