论文标题
雷达混乱分类的学习策略
Learning Strategies for Radar Clutter Classification
论文作者
论文摘要
在本文中，我们解决了将杂物返回分类以将其分类为统计均匀子集的问题。分类过程依赖于可观察到的模型，包括通过期望最大化算法求解的潜在变量。通过考虑三种不同的情况来实现杂物协方差矩阵的结构。初步性能分析强调，该提出的技术是将杂物聚集在整个范围内的可行手段。
In this paper, we address the problem of classifying clutter returns in order to partition them into statistically homogeneous subsets. The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm. The derivations are carried out by accounting for three different cases for the structure of the clutter covariance matrix. A preliminary performance analysis highlights that the proposed technique is a viable means to cluster clutter returns over the range.
