Kalman Filtering With Scheduled Measurements
Kalman Filtering With Scheduled Measurements
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DOI：
10.1109/tsp.2012.2235436
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发表时间：
2013-03
影响因子：
5.4
通讯作者：
Keyou You;Lihua Xie
中科院分区：
文献类型：
--
作者：
Keyou You;Lihua Xie
本文研究线性离散随机系统的传输调度和估计器的设计，以减少从传感器传输到估计器的测量量。为此，分别考虑了可控调度方案和不可控调度方案。作为系统测量的确定性函数，设计了一个可控调度程序，顺序决定测量向量的每个元素向估计器的传输。我们得到了一个近似的最小均方误差（MMSE）估计。另一方面，不可控调度意味着测量向量的传输是由一个与系统演化无关的随机过程驱动的。该调度下的MMSE估计量被转换为具有间歇观测值的卡尔曼滤波。给出了两个估计量的稳定性条件。最后，通过算例验证了理论结果。
This paper is concerned with the design of transmission scheduler and estimator for linear discrete-time stochastic systems to reduce the number of measurements to be transmitted from sensor to estimator. To this purpose, both controllable and uncontrollable scheduling schemes are considered, respectively. A controllable scheduler is designed as a deterministic function of system measurements, and sequentially decides the transmission of each element of a measurement vector to the estimator. We derive an approximate minimum mean square error (MMSE) estimator. On the other hand, an uncontrollable scheduler means that the transmission of the measurement vector is driven by a random process which is independent of system evolution. The MMSE estimator under this scheduler is cast as the Kalman filtering with intermittent observations. Some stability conditions are established for both the estimators. Finally, illustrative examples are included to validate the theoretical results.