Abstract

The problem of synthesizing optimal and quasi-optimal algorithms for complex information processing is solved using the methods of Markov theory for estimating random processes when maintaining a maneuvering object and two-channel vector observation with violations in statistically uncertain situations. The problem is solved in relation to a discrete-continuous Markov process for the case when its continuous part is a vector Markov sequence, and the discrete part is characterized by a three-component discrete Markov process, each component of which is described by a Markov chain to several positions. A block diagram of quasi-optimal complex information processing is given. Using a simple example, simulation modeling shows the performance of a quasi-optimal algorithm in statistically uncertain situations.

中文翻译：

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在统计不确定的情况下维护机动对象时测量的最佳复杂化

摘要

利用马尔可夫理论的方法来估计复杂信息处理的最优和准最优算法，用于在维护机动目标时估计随机过程和在统计不确定情况下进行双通道矢量观测。该问题针对离散-连续马尔可夫过程的连续部分是向量马尔可夫序列的情况进行了求解，离散部分的特征是三分量离散马尔可夫过程，其每个分量由马尔可夫描述链到几个位置。给出了准最优复杂信息处理的框图。使用一个简单的例子，仿真建模展示了准最优算法在统计不确定情况下的性能。