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

arXiv: https://arxiv.org/abs/2604.16008 · HTML: https://mlsanity.com/paper/2604.16008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Theoretical Analysis for Extended Target Recovery in Randomized Stepped Frequency Radars](https://mlsanity.com/paper/1908.02929/) (2019)

## Abstract (arXiv)

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.