Corner Reflector Array Jamming Discrimination Using Multi-Dimensional Micro-Motion Features with Frequency Agile Radar
This work addresses the problem of radar counter-countermeasures for naval defense, providing a robust discrimination method against a specific type of decoy jamming.
The paper proposes a method to distinguish real ship targets from corner-reflector-array jamming using frequency-agile radar by combining hand-crafted descriptors (MWR and CCF) with deep features from a lightweight CNN, achieving superior discrimination performance over state-of-the-art alternatives in simulations.
This paper introduces a robust discrimination method for distinguishing real ship targets from corner-reflector-array jamming with frequency-agile radar. The key idea is to exploit the multidimensional micro-motion signatures that separate rigid ships from non-rigid decoys. From Range-Velocity maps we derive two new hand-crafted descriptors-mean weighted residual (MWR) and complementary contrast factor (CCF) and fuse them with deep features learned by a lightweight CNN. An XGBoost classifier then gives the final decision. Extensive simulations show that the hybrid feature set consistently outperforms state-of-the-art alternatives, confirming the superiority of the proposed approach.