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[2602.18486] Support Vector Data Description for Radar Target Detection
Summary
This paper presents novel algorithms using Support Vector Data Description (SVDD) for radar target detection, addressing challenges posed by clutter and noise in radar environments.
Why It Matters
The research is significant as it proposes advanced methodologies for radar target detection, which is crucial in various applications like surveillance and navigation. By utilizing SVDD, the study aims to improve detection accuracy in complex environments, enhancing operational effectiveness in real-world scenarios.
Key Takeaways
- Traditional radar detection methods struggle with clutter and noise.
- SVDD and Deep SVDD offer robust alternatives for target detection.
- The proposed algorithms demonstrate effectiveness on simulated radar data.
- These methods can potentially enhance radar detection in complex environments.
- The research contributes to the field of machine learning and signal processing.
Computer Science > Machine Learning arXiv:2602.18486 (cs) [Submitted on 11 Feb 2026] Title:Support Vector Data Description for Radar Target Detection Authors:Jean Pinsolle, Yadang Alexis Rouzoumka, Chengfang Ren, Chistèle Morisseau, Jean-Philippe Ovarlez View a PDF of the paper titled Support Vector Data Description for Radar Target Detection, by Jean Pinsolle and 4 other authors View PDF HTML (experimental) Abstract:Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled by heavy-tailed distributions such as the Complex Elliptically Symmetric (CES) and Compound-Gaussian (CGD) families. Robust covariance estimators like M-estimators or Tyler's estimator address this issue, but still struggle when thermal noise combines with clutter. To overcome these challenges, we investigate the use of Support Vector Data Description (SVDD) and its deep extension, Deep SVDD, for target detection. These one-class learning methods avoid direct noise covariance estimation and are adapted here as CFAR detectors. We propose two novel SVDD-based detection algorithms and demonstrate their effectiveness on simulated radar data. Comments: Subjects: Machine Learning (cs.LG); Signal Processing (eess.SP); Machine Learning (stat.ML) Cite as: arXiv:2602.18486 [cs.LG] (or arXiv:2602.18486v1 [cs.LG] for th...
