北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (10): 2171-2180.doi: 10.13700/j.bh.1001-5965.2016.0836

• 论文 • 上一篇    

基于HMM的雷达状态转移估计方法

陈维高1, 贾鑫2, 朱卫纲2, 唐晓婧2   

  1. 1. 装备学院 研究生院, 北京 101416;
    2. 装备学院 光电装备系, 北京 101416
  • 收稿日期:2016-10-31 修回日期:2017-01-20 出版日期:2017-10-20 发布日期:2017-04-07
  • 通讯作者: 贾鑫 E-mail:13910413166@139.com
  • 作者简介:陈维高,男,博士研究生。主要研究方向:雷达信号获取与处理;贾鑫,男,硕士,教授,博士生导师。主要研究方向:空间信息对抗理论与技术、现代信号处理理论与应用;朱卫纲,女,博士,副教授,硕士生导师。主要研究方向:信息获取与处理;唐晓婧,女,硕士,助教。主要研究方向:信息获取与处理。

Radar state transfer estimation method based on HMM

CHEN Weigao1, JIA Xin2, ZHU Weigang2, TANG Xiaojing2   

  1. 1. Postgraduate School, Equipment Academy, Beijing 101416, China;
    2. Department of Photoelectric Equipment, Equipment Academy, Beijing 101416, China
  • Received:2016-10-31 Revised:2017-01-20 Online:2017-10-20 Published:2017-04-07

摘要: 针对传统识别模型存在的参数规律描述不全面的问题,提出一种适用于多功能雷达(MFR)的层级模型,该模型通过任务、状态、参数3个层级反映了MFR系统的运行机制,并依据不同的参数变化规律,设定多种函数进行描述,能够反映信号的联合变化和统计信息,较统计和脉冲样本图模型具备更好的识别效果。在层级模型基础上,针对MFR状态转移估计方法存在的鲁棒性、估计准确率不佳的问题,引入目标运动状态信息,构建双链隐马尔可夫模型(HMM),进而利用D-S (Dempster-Shafer)证据理论优化估计结果,提出一种基于HMM的雷达状态转移估计方法,实验结果表明,提出的方法较改进前具备更优异的鲁棒性和估计准确率。

关键词: 多功能雷达(MFR), 状态转移, 隐马尔可夫, 层级模型, D-S证据理论

Abstract: Aimed at the problem of traditional recognition models that parameter rule description is not complete, a hierarchical model suitable for multi-function radar (MFR) is proposed in this paper. This model reflects the operating mechanism of MFR system through three levels of task, state and parameter. Then according to parameter features, a variety of functions are used to describe the change rule of parameters, and signal joint changes and statistical information can be reflected. This model has better recognition effect compared with statistic and pulse sample diagram model. On the basis of the hierarchical model, to solve the problem of poor robustness and low accuracy of MFR state transfer estimation method, double chain hidden Markov model (HMM) was built by introducing target motion state information. D-S (Dempster-Shafer) evidence theory was used to optimize estimated results, and a radar state transfer estimation method based on HMM was proposed. The experimental results show that the proposed algorithm has more excellent robustness and higher estimation accuracy rate than that before improvement.

Key words: multi-function radar (MFR), state transition, hidden Markov, hierarchical model, D-S evidence theory

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