This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications.
Zhou et al. (Fri,) studied this question.