Hybrid optimization-driven color matching methodology for multi-spectral compatible camouflage materials

To achieve rapid and accurate color matching for multispectral camouflage coatings, this study established a color matching model based on the Kubelka–Munk two-constant theory system, utilizing six specialized camouflage pigments as base materials and developing a genetic algorithm-Levenberg–Marquardt collaborative optimization algorithm. The optimal concentrations of the six pigments were numerically simulated for the visible spectrum (400–700 nm). Composite pigments prepared with these optimized concentrations were fabricated into camouflage specimens. Experimental measurements indicated a 93.2% spectral reflectance fitting degree between the specimens and target colors across the 400–700 nm wavelength range, with a color difference (ΔE) of 0.86. Radar reflectivity tests demonstrated that the camouflage samples achieved radar wave absorption rates exceeding 90% over 37.8% of the tested electromagnetic frequency bandwidth (2–18 GHz), satisfying radar band camouflage requirements. Infrared imaging evaluations revealed that thermal infrared images of the camouflage specimens closely resembled those of natural rocks, with a mean radiation temperature difference of −0.57 °C meeting infrared camouflage specifications. These results collectively validate the multispectral compatibility of the proposed color matching scheme across visible, radar, and infrared wavebands. The developed collaborative optimization algorithm provides a reliable technical pathway for rapid development of complex camouflage coatings, demonstrating significant application potential in military engineering fields.