用支持向量数据描述提升雷达目标检测在复杂杂波下的性能
Support Vector Data Description for Radar Target Detection
- 采用单类学习的SVDD方法，避开噪声协方差估计难题
- 在模拟雷达数据上实现优于传统方法的检测率，尤其在重尾分布杂波下
- 适合处理热噪声与杂波混合场景的雷达系统设计者
经典雷达检测依赖自适应检测器，通过无目标的辅助数据估计噪声协方差矩阵。这类方法在高斯环境下有效，但在杂波（更适于用复椭球对称或复合高斯分布建模）存在时性能下降。鲁棒协方差估计器如M-估计或Tyler估计虽能缓解问题，但在热噪声与杂波共存时仍受限。为此，本文研究支持向量数据描述（SVDD）及其深度扩展——深嵌式SVDD（Deep SVDD）用于目标检测，这些单类学习方法无需直接估计协方差，并被改造为恒虚警率（CFAR）检测器。提出两种新型基于SVDD的检测算法，并在模拟雷达数据上验证其有效性。
原文摘要 · Abstract (English)
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.
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