一种改进的事件驱动的MFR雷达字提取方法
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摘要
从多功能雷达(MFR)脉冲序列中准确提取出雷达字是实现MFR行为认知的前提。作为脉冲序列分析的重要组成部分,现有的雷达字提取方法仅仅利用了脉冲到达时间这一信息,难以适应多参数灵活变化的复杂雷达信号。以传统的事件驱动方法为基础,提出一种改进的MFR的雷达字提取方法,对侦收到雷达脉冲依次进行量化、编码、计算事件概率,最后利用决策准则提取对应的雷达字。利用模拟生成的MFR脉冲序列对算法进行仿真,结果表明,所提方法能够有效提高提取准确率,且对虚假脉冲和漏脉冲有良好的适应性。该算法的应用能够为后续对MFR行为的准确辨识提供支撑。
Radar word extraction is the base for multi-function radar(MFR) behaviour cognition from the intercepted pulse sequence. As a crucial part in the pulse sequence analysis, common methods only utilize the time-of-arrival(TOA) of pulses, which perform not well when dealing with the complex signals with multiple parameters changing. Based on the traditional event-driven method, an improved approach is proposed, and radar words can be extracted by the steps of quantization coding, event probability calculation and word extraction decision. Simulation with hypothetical MFR signal data is presented, showing that the proposed method is effective and not sensitive to the missed or spur pulses. The extracted radar words can be used in MFR state recognition to support the adaptive radar countermeasures.
Radar word extraction is the base for multi-function radar(MFR) behaviour cognition from the intercepted pulse sequence. As a crucial part in the pulse sequence analysis, common methods only utilize the time-of-arrival(TOA) of pulses, which perform not well when dealing with the complex signals with multiple parameters changing. Based on the traditional event-driven method, an improved approach is proposed, and radar words can be extracted by the steps of quantization coding, event probability calculation and word extraction decision. Simulation with hypothetical MFR signal data is presented, showing that the proposed method is effective and not sensitive to the missed or spur pulses. The extracted radar words can be used in MFR state recognition to support the adaptive radar countermeasures.
引文
[1] BUTLER J. Tracking and control in multik-function radar[D]. London: University of London, 1998.
[2] CHARLISH A. Autonomous agents for multi-function radar resource management[D]. London: University of London, 2011.
[3] VISNEVSKI N, KRISHNAMURTHY V, HAYKIN S, et a1. Multi-function radar emitter modelling: a stochastic discrete event system approach[C]// Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, HI: IEEE Press, 2003: 6295-6300.
[4] VISNEVSKI N, KRISHNAMURTHY V, WANG A, et al. Syntactic modeling and signal processing of multifunction radars: a stochastic context-free grammar approach[J]. Proceedings of the IEEE, 2007, 95(5): 1000-1025.
[5] WANG A, KRISHNAMURTHY V, DILKES F, et al. Threat estimation by electronic surveillance of multifunction radars : a stochastic context free grammar approach[C]// Proceedings of the 45th IEEE Conference on Decision & Control. San Diego, CA: IEEE Press, 2006.
[6] VISNEVSKI N, HAYKIN S, KRISHNAMURTHY V, et al. Hidden markov models for radar pulse train analysis in electronic warfare[C]// IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, PA: IEEE Press, 2005(5): 597-600.
[7] VISNEVSKI N. Syntactic modeling of multi-function radars[D]. Hamiltion: McMaster University, 2005.
[8] LIU Haijun, LI Yue, LIU Zheng, et al. Approach to multi-function radar identification based on stochastic grammars[J]. Acta Aeronautica et Astronautica Sinica, 2010(31):1809-1817.
[9] LI Cheng, WANG Wei, WANG Xuesong. A method for extracting radar words of multi-function radar at data level[C]// IET International Radar Conference. Xi′an: IET Press, 2013.
[2] CHARLISH A. Autonomous agents for multi-function radar resource management[D]. London: University of London, 2011.
[3] VISNEVSKI N, KRISHNAMURTHY V, HAYKIN S, et a1. Multi-function radar emitter modelling: a stochastic discrete event system approach[C]// Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, HI: IEEE Press, 2003: 6295-6300.
[4] VISNEVSKI N, KRISHNAMURTHY V, WANG A, et al. Syntactic modeling and signal processing of multifunction radars: a stochastic context-free grammar approach[J]. Proceedings of the IEEE, 2007, 95(5): 1000-1025.
[5] WANG A, KRISHNAMURTHY V, DILKES F, et al. Threat estimation by electronic surveillance of multifunction radars : a stochastic context free grammar approach[C]// Proceedings of the 45th IEEE Conference on Decision & Control. San Diego, CA: IEEE Press, 2006.
[6] VISNEVSKI N, HAYKIN S, KRISHNAMURTHY V, et al. Hidden markov models for radar pulse train analysis in electronic warfare[C]// IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, PA: IEEE Press, 2005(5): 597-600.
[7] VISNEVSKI N. Syntactic modeling of multi-function radars[D]. Hamiltion: McMaster University, 2005.
[8] LIU Haijun, LI Yue, LIU Zheng, et al. Approach to multi-function radar identification based on stochastic grammars[J]. Acta Aeronautica et Astronautica Sinica, 2010(31):1809-1817.
[9] LI Cheng, WANG Wei, WANG Xuesong. A method for extracting radar words of multi-function radar at data level[C]// IET International Radar Conference. Xi′an: IET Press, 2013.