基于局部K-距离的靶场异常数据检测算法

航天靶场观测数据是鉴定运载火箭性能的重要依据，数据中的异常值严重影响数据处理的质量.传统的靶场异常数据处理方法不能适应日益提高的精度要求.为了解决这一问题，文章分析了测量数据中粗大误差的特点，提出了一种适合靶场观测数据的基于局部K-距离的异常数据检测算法LKD（Local K-Distance）.该算法通过计算对象与最近k个最近邻中的最大距离来分析数据对象的稀疏程度，从而检测异常值.实验结果证明，该方法简单快速，对粗大误差的检测有效率可达90%以上.

A new agorithm for outlier detection in rocketdrome based on local K-distance

The missile-drone experiment data is important measure to evaluation the performance of the carrier rockets.The abnormal data decreases the quality of data processing.Traditional methods can not meet the increasing demand of precision.To solve the problem,this study analyzed the characteristics of the thick errors,and proposed a new algorithm named LKD(Local K-Distance) to fit the requirement of rocketdrome.It can detect the outliers based on local K-Distance.It computes results the max distance between spe...