Abstract
Swarm formulations are new derivations of uncrewed aerial vehicles (UAVs) applications with more extensive application potentials. Radar signature characters of noncooperative UAV swarm targets require a more comprehensive understanding. The multimodality property of dynamic swarm targets proposes new challenges to restudy their electromagnetic scattering signatures. A larger number of unknowns make it difficult for existing full-wave numerical solvers to model their frequency response of radar cross section (RCS). This article introduces a solution to restore swarm target electromagnetic scattering signatures efficiently under sweep-frequency conditions. Swarm targets are first modeled by equivalent principle analysis (EPA) as compositions of multiple uniform equivalent surfaces enclosing each swarm unit. Variational formulations of EPA models extract frequency-dependent terms for computation redundancy reduction. The reduced-basis method (RBM) calculates reduced-basis functions from training solution datasets. Frequency responses of electromagnetic scatterings at an arbitrary frequency point are restored as a linear composition of reduced-basis functions. Unknown transformations from surface currents to expansion coefficients elevate the solution restoration efficiency prominently. Numerical results for three representative low-altitude noncooperative swarm targets verify the RCS restoration accuracy and efficiency. More discussions are addressed to explore factors that influence reduced-basis numbers. Existing results indicate that the proposed method is applicable to study swarm target frequency responses at an arbitrary modality. Limitations and future works are discussed with respect to restoration efficiency and accuracy optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 13729-13741 |
| Number of pages | 13 |
| Journal | IEEE Sensors Journal |
| Volume | 25 |
| Issue number | 8 |
| DOIs | |
| State | Published - 2025 |
Keywords
- Electromagnetic scattering
- equivalent principle algorithm
- radar cross section (RCS)
- reduced-basis method (RBM)
- uncrewed aerial vehicles (UAVs)
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