Research on the Algorithm for Grouping Battlefield Targets

In order to solve the problem of battlefield targets grouping, based on the research on targets key properties, this paper puts forward a method about using stable center of gravity algorithm based on entropy weight. The K-means clustering algorithms advantages and defects are introduced, and the paper makes improvement to the algorithm for its defects. For the difficulty of Selecting of the initial clustering centers, we put forward the solution of using experience knowledge which means we select the corresponding initial clustering centers according to the specific application scenario. And the research objects of this article are battlefield units. We propose to select the carrier, planes and other important units as the initial clustering centers and establish objects grouping model based on priori knowledge. Later in the experimental section, there is a comparison between the methodwe proposed and the traditional K-means algorithm, and we make a summary which proves the effectiveness of the method proposed.