密度聚类融合小子样统计的观瞄系统状态评估

State Evaluation of the Sighting System Based on Density Clustering and Small Sample Statistics

为解决武器观瞄系统受多种不确定因素的影响难以测得大量数据,文中提出一种新的状态评估方法。使用局部聚类算法对已采集到的数据进行聚类,得到各个等级聚类中心及其分类,再通过小子样统计方法将小样本转换成大样本,解决了试验样本随机性和样本不足性对评估模型的影响。实例分析表明,经该方法建立的模型得到的结论与基于先验知识的判断一致,验证了所提方法的有效性。

In order to solve the problem that the weapon sighting system is difficult to measure a large amount of data due to various uncertain factors,a new state assessment method is proposed.Use the local clustering algorithm to cluster the collected data to obtain the cluster centers of various levels and their classification.Then,the small sample is converted into a large sample by the small sample statistical method,which solves the impact of randomness and insufficient samples on the evaluation model.The example analysis shows that the conclusions obtained by the model established by this method are consistent with the judgment based on prior knowledge,which verifies the effectiveness of the proposed method.