β-分布的渐近抽样法及其在计算机上的实现

迄今,在一般的随机模拟文献中,关于β分布的抽样主要讨论参数为正整数情形。本文借助于Polya罐子模型黑球比例数X_的极限分布为β—分布这一原理,提出了正有理参数β—分布的渐近抽样方法,给出了模拟算法,编制了程序。选取若干正有理数参数,在计算机上进行了随机模拟,产生了渐近于β—分布的随机数,并制成了随机数表。本文对其中一些样本应用各种统计方法进行了检验,结果表明,拟合性是非常好的。     

A non-Bayesian segmenting tracker for highly maneuvering targets

The segmenting track identifier (STI) is introduced as a new methodology for tracking highly maneuvering targets. This nonBayesian approach dynamically partitions a target track into a sequence of track segments, making hard estimates of when the target's maneuvering mode transitions occur, and then estimates the parameters of the target model for each segment. STI is compared with two variable structures interacting multiple model (VS-IMM) algorithms through simulations, where it is shown to have a three fold performance advantage in median absolute turn rate estimation errors, as well as better position estimation for very highly maneuvering targets. STI is also shown to outperform a Rauch-Tung-Striebel (RTS) fixed-interval smoother when estimates are retrospectively derived, and STI accurately characterize the temporal pattern of maneuvers.