计算机应用 ›› 2017, Vol. 37 ›› Issue (2): 608-612.DOI: 10.11772/j.issn.1001-9081.2017.02.0608

• 应用前沿、交叉与综合 • 上一篇    

基于随机无穷自动机的多功能雷达辐射源识别方法

曹帅, 王布宏, 李龙军, 刘帅琦   

  1. 空军工程大学 信息与导航学院, 西安 710077
  • 收稿日期:2016-08-04 修回日期:2016-09-06 出版日期:2017-02-10 发布日期:2017-02-11
  • 通讯作者: 曹帅,465782523@qq.com
  • 作者简介:曹帅(1991-),男,陕西西安人,硕士研究生,主要研究方向:多功能雷达告警、句法模式识别;王布宏(1975-),男,山西太原人,教授,博士生导师,博士,主要研究方向:阵列信号处理、网络对抗、多功能雷达告警;李龙军(1988-),男,江西南昌人,博士研究生,主要研究方向:阵列信号处理;刘帅琦(1992-),女,陕西咸阳人,硕士研究生,主要研究方向:多输入多输出雷达信号处理。
  • 基金资助:
    国家自然科学基金资助项目(61172148)。

Multi-function radar emitter identification based on stochastic infinite automaton

CAO Shuai, WANG Buhong, LI Longjun, LIU Shuaiqi   

  1. Information and Navigation College, Air Force Engineering University, Xi'an Shaanxi 710077, China
  • Received:2016-08-04 Revised:2016-09-06 Online:2017-02-10 Published:2017-02-11
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61172148)

摘要: 针对基于随机上下文无关文法(SCFG)建模的多功能雷达(MFR)辐射源识别问题,提出了一种基于随机无穷自动机(SISA)的MFR辐射源识别方法。在文法建模的基础上,对“水星”MFR控制模块文法产生式和系统特征文法产生式进行重新构造生成SCFG,利用SCFG构造随机无穷自动机作为识别器,从而实现对测量辐射源的识别。通过理论分析和实验仿真得出:该方法能实现对MFR辐射源的识别;在一定范围内,通过增加文法产生式个数,可以提高平均识别率,且识别性能优于通过SCFG构造的随机下推自动机(SPDA)。实验结果表明了该方法的正确性和有效性。

关键词: 随机上下文无关文法, 多功能雷达, 辐射源识别, 随机无穷自动机, 文法产生式

Abstract: To deal with the emitter identification problem in Multi-Function Radar (MFR) based on Stochastic Context-Free Grammar (SCFG) model, a MFR emitter identification method based on Stochastic Infinite State Automata (SISA) was proposed on the basis of syntactic modeling. The grammar production rules in "Mercury" MFR control module and the characteristic production rules in "Mercury" MFR system were used in this method to reconstruct an SCFG, which was further used to construct an SISA for identification subsequently. Theoretical analysis and simulation results show that the proposed method can realize MFR emitter identification. Within a certain range, the average recognition rate can be improved by adding the amount of grammar production rules, and the identification performance is superior to Stochastic Push-Down Automata (SPDA) constructed by SCFG. The experimental results validate the reliability and effectiveness of the proposed method.

Key words: Stochastic Context Free Grammar (SCFG), Multi-Function Radar (MFR), emitter identification, stochastic infinite automaton, grammar production rule

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