We present an algorithm for estimating the state ofMarkov jump processes, given the counting observations. A characteristic feature of the class of considered observation systems is that the frequency of jumps in incoming observations significantly exceeds the intensity of the change of states of the estimated process. This property makes it possible for the filtering algorithm to process incoming observations using their diffusion approximation. The estimates proposed in this work have the stability property concerning inaccurate knowledge of the distribution of the observed process. To illustrate the robust qualities of the estimates, we present a solution for the applied problem of monitoring the state of an RTP connection based on observations of the packet flow recorded at the receiving node.

给定计数观察结果，我们提出了一种估计马尔可夫跳跃过程状态的算法。所考虑的观测系统类别的一个特征是，传入观测中的跳跃频率大大超过了估计过程状态变化的强度。此属性使过滤算法可以使用其扩散近似来处理输入的观测值。这项工作中提出的估计具有稳定性，涉及到对所观察到的过程的分布的不正确了解。为了说明估计的鲁棒质量，我们提出了一种基于在接收节点记录的数据包流的观察来监视RTP连接状态的应用问题的解决方案。