摘要
分析现有情报处理中传感器误差导致的融合精度性能下降问题,提出了基于大数据的传感器特征知识提取总体框架,在对指定区域情报大数据传感器目标关联基础上,建立基于点估计的传感器特征挖掘算法,实验室大数据仿真验证表明,论文提出的方法可有效分析传感器误差特征,可为提高战场情报数据融合传感器特征处理的自动化、智能化水平提供借鉴。
The performance degradation of fusion accuracy caused by sensor error in current intelligence processing is ana⁃lyzed,the overall framework of sensor feature knowledge extraction based on big data is proposed.On the basis of target association of big data sensor in designated area,a sensor feature mining algorithm based on high-precision target reference is established.Lab⁃oratory big data simulation results show that the proposed method can effectively analyze the sensor error characteristics,and can provide reference for improving the automation and intelligence level of sensor feature processing in battlefield intelligence data fu⁃sion.
作者
陆光宇
LU Guangyu(Marine Information Bureau,Beijing 100841)
出处
《舰船电子工程》
2020年第8期61-64,117,共5页
Ship Electronic Engineering
关键词
传感器误差
轨迹聚类
特征挖掘
sensor error
trajectory clustering
feature mining
