Efficient Multiscale Computation of Radar Cross Section for Large-Scale Chaff Clouds

Accurate and efficient Radar Cross Section (RCS) computation for chaff clouds is critical in electronic warfare. This task is challenging due to the massive scale of these clouds, complex element distributions, and intricate electromagnetic interactions, particularly mutual coupling and screening. This paper proposes the Multiscale Chaff Cloud RCS (MCC-RCS) method, a framework for computationally efficient RCS prediction of large-scale chaff clouds. It is specifically developed for clouds with arbitrary spatial but statistically uniform orientation distributions, while the underlying framework is extensible to other orientation models and bistatic scenarios. Its high computational speed and modest resource demands make it suitable for standard desktop computers, enabling rapid iterative computations such as echo simulation or chaff mixture optimization. The method employs a hierarchical multiscale strategy. At the microscale, it utilizes Einarsson’s formulation for the RCS of a single chaff dipole. The mesoscale implements a model combining mutual coupling with an intra-cell screening model. At the macroscale, an octree-based spatial decomposition provides efficient management and systematic accumulation of inter-cell screening. Numerical simulations demonstrate that MCC-RCS computes the monostatic RCS for clouds of up to 106elements within tens of seconds on a standard desktop with low memory usage, representing a significant speedup of several orders of magnitude compared against reported performance benchmarks for reference methods. The method shows good agreement with full-wave Method of Moments (MoM) for sparse distributions, and its accuracy for large-scale clouds is comparable to established Vector Radiative Transfer (VRT) methods, exhibiting physically consistent behavior. While high-fidelity solvers like Sparsification via Neglecting Far-field Coupling (S-NFC) offer superior accuracy, MCC-RCS provides a faster, less resource-intensive alternative, ideal for rapid large-scale assessments with a calibrated and acceptable trade-off between speed and accuracy.