Abstract Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches.

中文翻译：

---

一种处理雷达无人机检测复杂数据分布的三步分类框架

摘要 无人驾驶飞行器（UAV）应用广泛，成为越来越重要的雷达目标。为了更好地对雷达数据建模并解决维度灾难，提出了一种用于无人机检测的三步分类框架。首先，我们建议利用贪婪子空间聚类来处理潜在的异常值和雷达数据的复杂样本分布。由于训练样本不足和维数高，无法可靠估计所得多高斯模型的参数，尤其是协方差矩阵。因此，在第二步中，提出了多高斯子空间可靠性分析来处理这些协方差矩阵的不可靠特征维度。为了解决使用复杂多高斯模型对样本进行分类的挑战，并将样本的距离融合到不同维度的不同集群，在第三步中提出了子空间融合方案。所提出的方法在大型基准数据集上得到验证，其性能明显优于最先进的方法。