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Kalman filter-based identification for systems with randomly missing measurements in a network environment

机译:对于网络环境中随机丢失测量值的系统,基于卡尔曼滤波器的识别

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摘要

We consider the problem of parameter estimation and output estimation for systems in a transmission control protocol (TCP) based network environment. As a result of networked-induced time delays and packet loss, the input and output data are inevitably subject to randomly missing data. Based on the available incomplete data, we first model the input and output missing data as two separate Bernoulli processes characterised by probabilities of missing data, then a missing output estimator is designed, and finally we develop a recursive algorithm for parameter estimation by modifying the Kalman filter-based algorithm. Under the stochastic framework, convergence properties of both the parameter estimation and output estimation are established. Simulation results illustrate the effectiveness of the proposed algorithms.
机译:我们考虑在基于传输控制协议(TCP)的网络环境中系统的参数估计和输出估计的问题。由于网络引起的时间延迟和数据包丢失,输入和输出数据不可避免地会受到随机丢失的数据的影响。基于可用的不完整数据,我们首先将输入和输出缺失数据建模为两个独立的伯努利过程,其特征在于缺失数据的概率,然后设计缺失输出估计器,最后我们通过修改卡尔曼算法开发用于参数估计的递归算法。基于过滤器的算法。在随机框架下,建立了参数估计和输出估计的收敛性质。仿真结果说明了所提算法的有效性。

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