📄 中文摘要
传统的雷达检测技术依赖于自适应检测器，从无目标的次级数据中估计噪声协方差矩阵。这些方法在高斯环境中有效，但在存在杂波时性能下降，杂波更适合用重尾分布建模，如复椭圆对称（CES）和复高斯（CGD）族。尽管鲁棒协方差估计器如M估计器或Tyler估计器可以解决这一问题，但在热噪声与杂波结合时仍然面临挑战。为了解决这些问题，研究采用了支持向量数据描述（SVDD）及其深度扩展Deep SVDD进行目标检测。这些单类学习方法避免了直接估计噪声协方差，从而提高了检测性能。
基于支持向量数据描述的雷达目标检测
出处: Support Vector Data Description for Radar Target Detection
发布: 2026年2月24日
📄 English Summary
Support Vector Data Description for Radar Target Detection
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, the use of Support Vector Data Description (SVDD) and its deep extension, Deep SVDD, for target detection is investigated. These one-class learning methods avoid direct noise covariance estimation, thereby enhancing detection performance.