Intelligent Radar Detection Performance Assessment Based on Measurement Discrepancies

As pivotal components in intelligent technological ecosystems, radar systems ensure critical safety and operational efficiency across domains-from vehicular safety to meteorological monitoring-through their precision detection capabilities. This study proposes a data-driven intelligent framework for radar detection performance assessment based on leveraging measurement discrepancies (discrepancies between detected and ground-truth values) from radar detection experiments, aiming to establish a comprehensive analytical workflow encompassing data preprocessing, feature extraction, and trend prediction. The research focuses on characterizing radar detection capabilities in complex electromagnetic environments and introduces an intelligent evaluation methodology validated with real experiment data. By addressing the limitations of conventional approaches in modeling nonstationary time-series data, this framework enriches radar performance assessment theory, accelerates testing cycles, and reduces resource demands, thereby providing robust support for radar system optimization in military and aerospace applications.