改进cell密度聚类算法在空战目标分群中的应用

针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将...

Air combat target grouping based on improved CBSCAN algorithm

Aiming at the shortcomings of the traditional clustering algorithm on the clustering effect of manifold data, and the low real-time performance, the improved CBSCAN (cell-based density spatial clustering of applications with noise) was proposed to solve air combat target grouping issue. By analyzing the air combat situation parameters, the general model of air combat target grouping was established and the target grouping was transformed into clustering problem. Then, the target grouping model based on improved CBSCAN was established by improving the clustering method of CBSCAN algorithm. Through simulation experiments, the clustering accuracy and real-time performance of K-means, expectation maximum algorithm, density peak algorithm, density-based spatial clustering of applications with noise algorithm, CBSCAN algorithm and improved CBSCAN algorithm in 30 combat situations were compared and analyzed. The results show that the improved CBSCAN algorithm can correctly group multi-target formations under the condition of unknown number of formations and target manifold distribution, and the real-time performance was improved by about 30% compared with the original algorithm, which shows the practical application value of the proposed method.