Efficient UAV Swarm Target Scattering Response Restoration for Radar Sensors with Optimized Empirical Interpolation Method

Swarm formulations of Uncrewed Aerial Vehicles (UAV) and birds bring new challenges for radar sensors in remote sensing problems with ambiguous modality identification performance. Multimodality property brings higher radar signature complexity and uncertainties. Simulations of scattering responses for multimodality swarm targets are fundamental to support advanced radar signature explorations. Equivalent Principle Algorithm (EPA) and Empirical Interpolation Method (EIM) are integrated intelligently to breakthrough computation resource bottlenecks in conventional full-wave electromagnetic solvers with acceptable errors. This data driven solution enables massive RCS simulations for swarm targets efficiently. The conventional EIM is confined by quantity consistencies between extracted interpolation points and basis function. It limits scattering solution space spanning capability for swarm structures with higher complexities. This paper optimizes criteria for basis function and interpolation point selections. Basis orthogonality is applied to elevate space spanning capability. More interpolation points are extracted with Fruit Fly Optimization Algorithm (FOA) to cover solution space. Solution restoration framework is reformulated. Numerical results indicate that the new method restores scattering responses of swarm targets with higher accuracy. More interpolation points have minor impact on efficiency to guarantee its edge computing applicability for radar sensors.