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文献[1]给出了在距离采样为非独立时秩值量化器的P_(FA),但因P_(FA)表示为一个多重积分,其积分重数等于采样数,当采样数大于8时计算P_(FA)就很困难。本文所介绍的方法并不受采样数的限制,而且还可以得到非起伏目标的发现概率P_D。文中用DJS-6计算机产生一组窄带的Rice型一阶Markov随机数列,并用MonteCarlo模拟方法得出相关Gauss噪声中秩值检测器的特性。
Literature [1] gives the P_ (FA) of rank-valued quantizer when distance samples are not independent, but because P_ (FA) expresses as a multiple integral, the integral weight is equal to the number of samples. When the number of samples is greater than 8 Calculating P_ (FA) is difficult. The method described in this paper is not limited by the number of samples, but also can get the discovery probability P_D of non-undulation target. In this paper, a set of narrow-band Rice-type first-order Markov random numbers is generated by the DJS-6 computer. The Monte Carlo simulation method is used to obtain the characteristics of the rank detector in Gaussian noise.