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[2602.13296] MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models
Summary
This paper presents a novel approach to evaluating high-resolution range profile (HRRP) data using MFN decomposition, addressing challenges in generative model evaluation for radar automatic target recognition.
Why It Matters
As radar technology advances, the ability to accurately classify and evaluate generated HRRP data is crucial for improving automatic target recognition systems. This research proposes new metrics that enhance explainability and evaluation depth, which are essential for the development of reliable AI systems in defense and surveillance applications.
Key Takeaways
- Introduces MFN decomposition to analyze HRRP data effectively.
- Proposes two new metrics for evaluating generative models in radar applications.
- Demonstrates the discriminative capability of the proposed metrics using a challenging dataset.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13296 (cs) [Submitted on 9 Feb 2026] Title:MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models Authors:Edwyn Brient (CMM), Santiago Velasco-Forero (CMM), Rami Kassab View a PDF of the paper titled MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models, by Edwyn Brient (CMM) and 2 other authors View PDF Abstract:High-resolution range profile (HRRP ) data are in vogue in radar automatic target recognition (RATR). With the interest in classifying models using HRRP, filling gaps in datasets using generative models has recently received promising contributions. Evaluating generated data is a challenging topic, even for explicit data like face images. However, the evaluation methods used in the state-ofthe-art of HRRP generation rely on classification models. Such models, called ''black-box'', do not allow either explainability on generated data or multi-level evaluation. This work focuses on decomposing HRRP data into three components: the mask, the features, and the noise. Using this decomposition, we propose two metrics based on the physical interpretation of those data. We take profit from an expensive dataset to evaluate our metrics on a challenging task and demonstrate the discriminative ability of those. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2602.13296 [cs.CV] (or arX...
