Dynamic Radar Cross-Section Estimation of Chaff Clouds Based on a Surrogate Model for Spatiotemporal Distribution

This paper presents a novel surrogate modeling approach for estimating the dynamic radar cross-section (RCS) of chaff clouds under diverse launch and environmental conditions. A high-fidelity computational fluid dynamic–discrete element method (CFD-DEM) framework is first used to simulate the multiphysics behavior of chaff clouds generated by both naval and aircraft dispensers. These simulations generate detailed aerodynamic datasets, which are used to train a Gaussian process regression (GPR)–based surrogate model. The surrogate model enables efficient prediction of the spatiotemporal distribution of chaff clouds, incorporating variables such as wind speed, wind direction, and launch parameters. To estimate dynamic RCS, the spatiotemporal distributions are combined with approximation techniques, specifically the generalized equivalent conductor (GEC) and vector radiative transfer (VRT) methods. A real-time chaff cloud simulator with a graphical user interface is also developed, integrating aerodynamic modeling, RCS calculations, and signal fluctuation modeling. Simulation results demonstrate that the proposed surrogate model achieves high prediction accuracy, with normalized mean absolute errors (NMAE) of 0.0085 for naval chaff and 0.0176 for aircraft chaff. The dynamic RCS obtained via the surrogate model closely matches the CFD-DEM results while substantially reducing computational cost, thus offering practical utility for real-time system applications.