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在不同探测环境下确定探测概率时 ,首先需要根据给定的虚警率来确定相应的探测概率门限因子 ,这时需要在杂波和被检测单元的概率分布密度函数已知条件下 ,对其进行积分 .实际应用中 ,概率分布密度函数非常复杂 ,这时直接积分法很难奏效 ,必须用数值模拟法来解决 .但常规的 Monte Carlo方法在模拟像虚警概率这类小概率事件时既耗费机时 ,精度又差 ,目前国际上正在研究应用重要度抽样法(Importance Sampling)来解决该问题 .本文在前人研究成果的基础上 ,针对两大类雷达探测处理器——单元平均恒虚警 (CA-CFAR)和修整平均恒虚警处理器 (TM-CFAR) ,对相应的重要度抽样方法进行了研究和推导 ,结果表明方法高效可靠
In different probing environments to determine the probability of detection, the first need to be based on a given false alarm rate to determine the appropriate probing probability threshold factor, then the need to be detected and the probability density function of the unit under the known density distribution, its In practical applications, the probability distribution density function is very complex, then the direct integration method is difficult to work and must be solved by the numerical simulation method, but the conventional Monte Carlo method simulates such small probability events as the false alarm probability At the present time, the research on the Importance Sampling is being researched to solve this problem.On the basis of the previous research results, this paper aims at two main types of radar detection processors-the average unit constant CA-CFAR and TM-CFAR, the corresponding importance sampling method is studied and deduced. The results show that the method is efficient and reliable