雷达目标检测中的支持向量数据描述
Support Vector Data Description for Radar Target Detection
AI总结：
本文提出基于支持向量数据描述的雷达目标检测方法，通过改进的单类学习算法提升在杂波环境下的检测性能。
摘要：
经典雷达检测技术依赖于自适应检测器，这些检测器从无目标的二次数据中估计噪声协方差矩阵。虽然在高斯环境中有效，但这些方法在存在杂波时性能下降，而杂波更适合用重尾分布如复椭圆分布（CES）和复合高斯分布（CGD）来建模。鲁棒协方差估计器如M估计量或泰勒估计量解决了这一问题，但在热噪声与杂波混合时仍存在困难。为克服这些挑战，我们研究了支持向量数据描述（SVDD）及其深度扩展Deep SVDD在目标检测中的应用。这些单类学习方法避免了直接噪声协方差估计，并在此被适配为CFAR检测器。我们提出了两种新的基于SVDD的检测算法，并在模拟雷达数据上展示了其有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
