电子学报 ›› 2016, Vol. 44 ›› Issue (2): 392-397.DOI: 10.3969/j.issn.0372-2112.2016.02.021

• 学术论文 • 上一篇    下一篇

基于文法派生解析表的多功能雷达快速参数估计方法

代鹂鹏1, 王布宏1, 沈海鸥1, 贾月岭2   

  1. 1. 空军工程大学信息与导航学院, 陕西西安 710077;
    2. 空军工程大学装备管理与安全工程学院, 陕西西安 710077
  • 收稿日期:2014-05-19 修回日期:2014-09-30 出版日期:2016-02-25
    • 作者简介:
    • 代鹂鹏 男,1989年1月出生于甘肃省平凉市.现为空军工程大学信息与导航学院硕士研究生.主要研究方向为多功能雷达信号处理.E-mail:dlipeng@163.com;王布宏 男,1975年12月出生于山西省太原市.现为空军工程大学教授、博士生导师.主要从事阵列信号处理、阵列校正等方面的研究工作.E-mail:wbhyl@aliyun.com;沈海鸥 女,1990年7月出生于甘肃省兰州市.现为空军工程大学信息与导航学院博士研究生.主要研究方向为阵列信号处理.E-mail:haioushen1990@sina.com
    • 基金资助:
    • 国家自然科学基金 (No.61172148)

Fast Parameter Estimation of Multi-function Radar Based on Syntactic Derivation of Parse Chart

DAI Li-peng1, WANG Bu-hong1, SHEN Hai-ou1, JIA Yue-ling2   

  1. 1. School of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi 710077, China;
    2. School of Equipment Management and Security Engineering, Air Force Engineering University, Xi'an, Shaanxi 710077, China
  • Received:2014-05-19 Revised:2014-09-30 Online:2016-02-25 Published:2016-02-25

摘要:

随机上下文无关文法(SCFG)在多功能雷达(MFR)状态识别和威胁估计中具有良好的应用前景.为了减少常规算法的运算复杂度,本文提出一种基于解析表构造的多功能雷达参数快速估计方法.该方法通过对截获的每个雷达数据序列构造库克-杨-卡塞米(CKY)解析表,排除了大量未参与序列派生过程的产生式,随后在解析表的基础上采用改进的Inside-Outside(IO)算法对雷达文法产生式概率和多功能雷达状态进行快速估计.理论分析与实验仿真证明,该算法在参数估计精度相同的条件下,其运算时间相对于常规IO算法和Viterbi-Score(VS)算法减少了50%以上.

关键词: 电子战, 多功能雷达, 随机上下文无关语法, 参数学习

Abstract:

Stochastic context-free grammar (SCFG) has a promising application prospect in the field of mode recognition and threat estimation of multi-function radars (MFR).The primary limitation of the existing learning algorithms is their huge computing complexity.A fast learning algorithm for the parameters of MFR grammar is proposed, in which the Cocke-Younger-Kasami(CKY) parsing chart is first pre-computed for each training sequence to delete the rules that are not involved in the signal generation.Finally, the estimation of radar grammar parameters is realized with a modified inside-outside (IO) algorithm.The computing complexity is theoretically analyzed, moreover, simulation experiments are provided to verify the algorithm efficiency.Compared with the conventional IO and Viterbi-score (VS) algorithms, more than half operation time is reduced with our proposed algorithm while the favorable estimation accuracy is maintained.

Key words: electronic warfare, multi-function radar, stochastic context free grammar, parameter learning

中图分类号: