Corner Reflector Array Jamming Discrimination Using Multi-Dimensional Micro-Motion Features with Frequency Agile Radar

TL;DR

This paper presents a novel method combining handcrafted micro-motion features and deep learning to effectively discriminate real ships from jamming decoys in frequency-agile radar systems.

Contribution

It introduces a hybrid feature set and a classification framework that significantly improves discrimination accuracy over existing methods.

Findings

Hybrid features outperform state-of-the-art alternatives.

The approach effectively distinguishes real ships from decoys.

Simulations confirm the method's robustness and superiority.

Abstract

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.