计算机与数字工程2023，Vol.51Issue(10)：2309-2312,2383,5.DOI:10.3969/j.issn.1672-9722.2023.10.018
基于SVM和GM-PHD的密集杂波环境下的数据处理算法
Data Processing Algorithm Based on SVM and GM-PHD in Dense Clutter Environment
摘要
Abstract
The problem of multi-target tracking(MTT)in a dense clutter environment has always been a difficult research point.Currently,the probability hypothesis density filtering(PHDF)algorithm is a hot research direction.In order to solve the prob-lem that the complexity and the error rate of GM-PHD increase significantly with strong clutter,an improved algorithm is proposed.Before using GM-PHD to calculate and update the target,the support vector machine technology is used to classify the measure-ment data.Through this technology,valid target data is retained while filtering clutter data as much as possible.The findings demon-strate that the algorithm effectively reduces the computational complexity,suppress clutter and improve tracking performance.关键词
多目标跟踪/概率假设密度滤波算法/支持向量机/数据处理/数据关联Key words
target tracking/PHD/SVM/data processing/data association分类
信息技术与安全科学引用本文复制引用
武忠鸣,郭剑辉,楼根铨,张文俊..基于SVM和GM-PHD的密集杂波环境下的数据处理算法[J].计算机与数字工程,2023,51(10):2309-2312,2383,5.基金项目
新疆建设兵团重点领域科技攻关项目(编号:2019BC010) （编号:2019BC010）
国家自然科学基金项目(编号:61603190)资助. （编号:61603190）
