Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

This study focuses on the reentry target classification and fuses features based generalized evidence theory. The are extensively investigated, ballistic factor length of high-resolution range profile selected. theory is advantageous for solving feature fusion, representing uncertainty, widely used in defense applications. However, determining basic probability assignment (GBPA) dealing with uncertainty a matter that requires further improvement. In this paper, we propose new method to determine GBPA using time-series radar data. First, samples each known class encoded as fuzzy number (GFN), power set comprising frame discernment (FOD) calculated from GFN intersection area. Subsequently, test triangular numbers, reflecting mean standard deviation Kalman filter. Finally, firing strength between model input degree support hypothesis, which GBPA. proposed algorithm compared existing methods exhibits high accuracy short time without leakage. experiments various uncertainties, results demonstrate our can effectively reflect