Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
通过模型聚类识别含杂波的空间点过程特征
We consider the problem of detecting features, such as minefields or seismic faults, in spatial point processes when there is substantial clutter. We use model-based clustering based on a mixture model for the process, in which features are assumed to generate points according to highly linear multivariate normal densities, and the clutter arises according to a spatial Poisson process. Nonlinear features are represented by several densities, giving a piecewise linear…