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

2026-04-17

AI Summary

This work addresses the challenging discrimination between real ships and corner reflector array decoys by proposing a multidimensional micro-motion feature fusion method based on frequency-agile radar. Micro-motion signatures are extracted from Range-Velocity maps, and two novel handcrafted features—Mean-Weighted Residual (MWR) and Complementary Contrast Factor (CCF)—are designed. These are combined with deep features extracted by a lightweight CNN to form a hybrid feature set, which is then fed into an XGBoost classifier for high-accuracy discrimination. Extensive simulations demonstrate that the proposed approach significantly outperforms existing state-of-the-art methods, substantially enhancing the capability to identify non-rigid decoys.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Corner Reflector Array Jamming
• Target Discrimination
• Micro-Motion Features
• Frequency Agile Radar
• Radar Countermeasures

Innovation

• micro-motion features
• frequency agile radar
• corner reflector array jamming
• hybrid feature fusion
• lightweight CNN