Method of Surface-to-Air Threat Information Fusion Using Bayesian Search Theory

This study presents a method to evaluate and fuse detection information for surface-to-air missile (SAM) threats based on Bayesian search theory and radar equations. Detection probabilities are computed using radar parameters, and spatial distributions are visualized through MATLAB simulations based on two radar warning receiver models. Results indicate that detection outcomes vary depending on threat type and receiver location. Mid-range SAMs show stable detection patterns, while long-range SAM presents low detection probability, limiting its geolocation accuracy. Bayesian updating is applied to integrate multi-source detection results, resulting in improved posterior localization for specific categories of surface-to-air missile systems distinguished by range and detection characteristics. This approach provides a quantitative framework for integrating surface-to-air threat information in operational environments and contributes to the development of future electronic warfare and air defense strategies.