波束内目标与诱饵的参数估计是导引头正确实现目标分选、完成波束指向调整与精确跟踪的必要条件.目标与诱饵的“紧密接近”导致接收回波混叠,使得常规参数测量与估计方法失效.基于实际采样处理中目标回波能量会“溢出”到相邻匹配滤波采样点这一信号模型,通过贝叶斯原理从观测的条件似然以及未知参数的先验分布获取待估计参数的后验概率分布,采用Markov Chain Monte Carlo (MCMC)方法中的Metropolis-Hastings (M-H)抽样算法联合估计目标与诱饵的相关参数,并根据拖曳式诱饵干扰对抗的特点对M-H抽样进行了改进.各种典型干扰条件及动态攻击场景下的仿真试验表明了本文方法的有效性.%The parameter estimation of the target and towed radar active decoy (TRAD) within the radar beam is the necessary condition for radar seeker to realize target selection and achieve the boresight steering adjust and accurate tracking. The "closely spaced" target and decoy make radar echoes abased and the traditional method for parameter measurement and estimation fail. Based on the signal model, that the energy of single target will spill over to adjacent matched filter sampling points when the output of matched filter of radar seeker is sampled actually. Bayesian theory is adopted to obtain the posterior probability density function of estimated parameters from the conditional likelihood function of the observation and the prior distribution of unknown parameters in this paper. Then the Metropolis-Hastings (M-H) sampling algorithm belonged to Markov Chain Monte Carlo (MCMC) is utilized to estimate parameters of target and decoy jointly. At the same time, based on the jamming model and characteristics of TRAD, the M-H sampling is improved. The simulation results under different jamming conditions and dynamic attack scenarios illustrate the performance of proposed method.
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