Multiplier-Free Robust Adaptive Beamforming for UAV Radars: A Double-Shift PoT Approach

Deploying robust adaptive beamforming (RABF) algorithms on Unmanned Aerial Vehicle (UAV) swarm radars presents a severe conflict between anti-jamming performance and hardware efficiency. Traditional RABF algorithms demand high-precision continuous floating-point weights, necessitating numerous power-hungry hardware multipliers (e.g., DSP slices on FPGAs) during the high-speed real-time spatial filtering stage. This is highly detrimental to the stringent Size, Weight, and Power (SWaP) constraints of edge UAVs. While naive single-shift Power-of-Two (PoT) quantization mitigates multiplier usage, it inevitably suffers from severe null-shallowing effects, failing to suppress strong active jammers. In this paper, we propose a multiplier-free hardware/algorithm co-design: the Double-Shift PoT RABF. By mapping the complex weights to a meticulously designed double-shift discrete dictionary, our approach entirely eradicates multipliers in the data path, substituting them with energy-efficient programmable bit-shifters and adders. We formulate the discrete optimization problem using the Alternating Direction Method of Multipliers (ADMM) framework to guarantee deep jamming nulls. Extensive simulations demonstrate that under UAV position perturbations, the proposed multiplier-free method exhibits nearly zero performance degradation (less than 0.1 dB SINR loss) compared to the traditional 32-bit floating-point baseline, offering a highly hardware-efficient solution for next-generation UAV edge computing.