【摘要】 对于海军舰队而言,反舰导弹是最主要的威胁之一,如何提高舰队对反舰导弹的防御能力已成为当今电子战研究热点。预测反舰导弹辐射源的行为并作出相应的防御策略是消除反舰导弹威胁的重要方式之一。由于贝叶斯分析是机器学习中的一种高效的推理算法,因此,以贝叶斯模型为基本框架,利用蒙特卡洛马尔可夫链(MCMC)方法,以及MCMC中常用的Metropolis-Hastings算法对反舰导弹辐射源的行为进行分析判断,为以后升级海军舰队的电子防御系统提供了基本研究方向。更多还原

【Abstract】 For the naval fleet,anti-ship missiles are one of its most important threats. How to improve the fleet’s defense capabilities against anti-ship missiles has become a hot spot in Electronic Warfare research. To reduce the threat of ASM,one important method is to predict the mode of its radar and to take relative defense strategies. Since Bayesian analysis is an efficient reasoning algorithm in machine learning,using Bayesian model,as the basic framework is a good choice to analyze the mode of the anti-ship missile radar on the framework of Bayesian Model by using the Monte Carlo Markov chain(MCMC)method and the Metropolis-Hastings algorithm commonly used in MCMC. That also provides the research direction of EP system of naval fleet.更多还原