Multi-Feature Fusion Based Radar Seeker Against Corner Reflector Interference

When a radar seeker is searching and tracking enemy land tanks, radar vehicles, or armored vehicles, it is often accompanied by false targets formed by corner reflectors. Strong false target interference will bring great difficulties to combat vehicle targets. In order to improve the radar's ability to identify vehicles, counter angular reflector interference has become an important research direction. Combined with the Radar seeker measured data, firstly, the Radar seeker target echo is extracted from the ground clutter. Secondly, the time-domain HRRP feature difference between the angular anti-jamming echo and the vehicle target echo is investigated, and then the polarization features are extracted based on the fully polarized echo data. Then the support vector machine is used to test the discrimination performance based on single-feature and multi-feature fusion based on the measured vehicle target and corner anti-jamming echo data in the field, respectively. The test results show that single-feature discrimination is not robust enough, while the proposed multi-feature fusion discrimination not only has higher robustness, but also achieves more than 85% discrimination accuracy under multiple sets of tests.