Efficient Electromagnetic Scattering Signature Restoration for UAV Swarm Targets Based on Equivalent Principle Algorithm and Empirical Interpolation Method

Swarm formulation is a new unmanned aerial vehicle (UAV) derivation with high application significance and military threats. UAV swarms are nonrigid deformable extended radar targets. Their multimodality characters bring higher radar cross section (RCS) variety and statistical uncertainty. Conventional full-wave electromagnetic solvers are problematic in resource consumption for swarm target RCS modelings and simulations. This article introduces an efficient UAV swarm RCS restoration framework by integrating the equivalent principle algorithm (EPA) and empirical interpolation method (EIM) with particular customization strategies. Each UAV within a swarm is enclosed by an equivalent surface to reduce meshing complexity and maintain unknown consistency. Interpolation points and basis functions are extracted from training spaces to restore electric and magnetic surface currents for arbitrary swarm modalities efficiently. New training parameter selection and solution approximation methods are developed to accommodate swarm target scattering problems. Quadcopter drone and fixed-wing UAV swarm models are developed for verification. The proposed method is validated and analyzed from multiple aspects. RCS results present good numerical consistency with benchmark results from simulation and measurement solutions. Its efficiency superiority is distinctive in both memory and time consumption compared with numerical solvers. This enables the RCS study for arbitrary dynamic UAV swarm configurations. Numerical performance dependence on UAV swarm configurations is studied to explore its applicability conditions. Training parameter optimization and EIM strategy modification are promising to elevate the RCS restoration accuracy and efficiency performance of the proposed framework.