A Radar Super Resolution Algorithm for Cluster UAV Targets Estimation

The development of unmanned aerial vehicles (UAVs) and their innovative "swarm" concept have presented a formidable challenge to public security and battlefield confrontation. Therefore, there is an urgent necessity for UAV detection technology to attain precise estimation of UAVs, thereby enabling comprehensive surveillance and assessment of clustered UAVs. In response to the detection demands posed by UAV swarms, this paper introduces a super-resolution method tailored for UAVs radar detection. Distinguishing from conventional spatial domain super-resolution approaches, this research formulates a convolution model specifically deduced within the Range-Doppler domain for swarm targets. Then, a sparse regularization method is utilized to construct the objective function for super-resolution estimation. Finally, a multiplicative iterative algorithm is employed to estimate the clustered targets within individual Range-Doppler units. The simulation outcomes have verified the effectiveness of proposed methodology.