Hybrid filter with predict-estimator and compensator for the linear time invariant delayed system
Hybrid filter with predict-estimator and compensator for the linear time invariant delayed system
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DOI：
10.1007/s11767-009-0071-x
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Chenglin Wen;Quanbo Ge;Xiaoliang Feng
中科院分区：
文献类型：
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作者：
Chenglin Wen;Quanbo Ge;Xiaoliang Feng
研究一类受通信容量限制的线性时不变系统的实时估计问题。网络环境中常见的通信限制包括信号传输延迟、测量乱序和数据包丢失等。滤波器设计的核心同样是将传统卡尔曼滤波器公式化为一个由初始状态估计和所有顺序采样测量组成的线性加权和。为了适应上述信息的限制，将线性加权求和分解为两个阶段。一种是由所有到达的测量数据组成的预测估计器，另一种是由这些时滞数据构造的补偿器。在网络环境下，新的混合滤波器与现有的延迟卡尔曼滤波器有明显的区别。例如，在所有测量值都可用的情况下，新型滤波器可以在线性均方误差最小的意义上是最优的，并且比现有的延迟滤波器具有最低的运行时间。通过一个包括两个案例的仿真来说明本文提出的设计步骤。
This paper investigates the problem of real-time estimation for one kind of linear time invariant systems which subject to limited communication capacity. The communication limitations include signal transmission delay, the out-of-sequence measurements and data packet dropout, which appear typically in a network environment. The kernel of filter design is equally to formularize the traditional Kalman filter as one linear weighted summation which is composed of the initial state estimate and all sequential sampled measurements. For it can adapt aforementioned information limitations, the linear weighted summation is then decomposed into two stages. One is a predict-estimator composed by all reached measurements, another is one compensator constructed by those time-delayed data. In the network environment, there are obvious differences between the new hybrid filter and those existing delayed Kalman filters. For example, the novel filter can be optimal in the sense of linear minimum mean square error as soon as all measurements available and has the lowest running time than these existing delayed filters. One simulation, including two cases, is utilized to illustrate the design procedures proposed in this paper.