Experimental Investigation of Distributed Array Adaptive Beamforming for Interference Suppression in UAV Swarms
Rio King, Gregory H. Huff, Trevor Bois, Bailey Campbell · Drones · 2026
This paper investigates the use of adaptive beamforming algorithms for communication systems and sensing networks using motion-dynamic distributed random arrays. These distributed arrays include swarms of unmanned aerial vehicles (UAVs) and are formed by unconnected antennas mounted on independent mobile platforms. This paper investigates the robustness of adaptive beamforming algorithms subject to nonidealities intrinsic to distributed random arrays such as positional error, hardware noise variations, and non-uniform elements. A simulation framework developed to evaluate various beamforming algorithms in the presence of non-idealities demonstrates that minimum variance distortionless response (MVDR) beamforming is sensitive to nominal positional errors, while minimum mean squared error (MMSE) beamforming maintains interference suppression regardless of positional error and is robust to non-uniform elements. Experiments confirm that MMSE beamforming demonstrates interference suppression in real-world channels with heterogeneous hardware. These results establish adaptive mean-squared-error-based beamforming as a robust solution for distributed random arrays.