一种航迹数据高维特征矩阵提取方法

Method for Extracting High-dimensional Feature Matrix of Track Data

针对雷达航迹数据特征提取不充分使得对空中目标分类识别准确率低的问题,提出了一种航迹数据高维特征矩阵提取方法。首先从机动性、巡航性、飞行区域以及高阶特征进行航迹数据分析,进而在不同维度统计数据特征、提取多维航迹数据特征参数,最终形成航迹数据高维特征矩阵。通过实测航迹数据实验表明对特征提取充分,多类机器学习方法验证识别率统计均值为92.4%,证明了本文算法的可行性与稳定性,该方法可作为提升航迹目标识别准确率的有效手段。

Aiming at the problem of insufficient feature extraction of radar trajectory data,the accuracy of air target classification and recognition is low.This paper presents a method for extracting high-dimensional feature matrix of track data.The article first analyzes the trajectory data from maneuverability,cruising,flight area and high-level features,then statistical data features in different dimensions,extracting multi-dimensional trajectory data feature parameters,and finally forming a high-dimensional feature matrix of trajectory data.Experiments with measured track data show that the feature extraction is sufficient.The statistical average of the recognition rate verified by multiple machine learning methods is 92.4%,which proves the feasibility and stability of the algorithm in this paper.This method can be used as an effective means to improve the accuracy of track target recognition.