State Estimation for Networked System with Random Delay Using Kalman Filter
State Estimation for Networked System with Random Delay Using Kalman Filter
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DOI：
10.5687/sss.2014.9
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
M. Yagi;Y. Sawada
中科院分区：
文献类型：
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作者：
M. Yagi;Y. Sawada
讨论了一类具有随机时滞观测数据的线性随机系统的状态估计器的构造方法。假设系统的状态变量是通过引起随机延迟的通信网络来观测的。在我们的研究中，随机延迟被假定为由平均延迟和随机性的总和。通过在平均时延后的时间附近进行泰勒级数展开，将具有随机时延的观测数据表示为具有状态依赖加性噪声的观测模型。该观测系统成为随机双线性系统。基于双线性观测系统，构造了卡尔曼滤波器作为线性随机系统的状态估计器。通过数值仿真，对正弦波、阶跃函数、方波等几种输入信号证明了该滤波器的有效性。
This paper discusses an approach of constructing a state estimator for a class of linear stochastic systems with randomly delayed observation data. State variables of the system is assumed to be observed over a communication network which causes the random delay. In our study, the random delay is assumed to consist of a sum of an average delay and a randomness. The observation data with the random delay can be represented by an observation model with state-dependent additive noise by using the Taylor series expansion around the time behind the average delay. This observation system becomes a stochastic bilinear system. The Kalman filter is constructed as the state estimator for the linear stochastic system based on the bilinear observation system. The effectiveness of the proposed Kalman filter is demonstrated for the several types of input signals such as a sinusoidal wave, a step function, a square wave and so on by numerical simulations.