摘要
复杂战场环境下,雷达信号经处理后输出的目标点迹信息仍会混杂大量的虚假剩余点迹,对目标自动起始和稳定跟踪带来挑战。提出了一种基于点迹综合特征评估的目标点迹处理的精细化设计方法,综合考虑目标与杂波等特征,采用机器学习中的支持向量机对目标和杂波进行分类判别。雷达实测数据验证结果表明,该方法对杂波的抑制率较传统方法有显著提升,方法有效。
In the complex battlefield environment,there still remain a large number of false plots in radar’s output plots after signal processed,thus it brings challenges to the target’s automatic initial and stable tracking.A precision design method of target plots processing based on comprehensive feature evaluation of point trace is proposed.The features of target and clutter are considered comprehensively,and the target and clutter are classified and discriminated by support vector machine support vector machine(SVM)in machine learning.The actual radar data show that this method is more effective than the traditional method in suppressing clutter.
作者
孙宜斌
SUN Yibin(The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210023,China)
出处
《指挥信息系统与技术》
2022年第1期69-73,共5页
Command Information System and Technology
基金
科技部国家重点研发计划(2020YFB1600100)资助项目。
关键词
精细化设计
支持向量机
机器学习
最优化
precision design
support vector machine(SVM)
machine learning
optimization
