Kalman filtering with intermittent observations
Kalman filtering with intermittent observations
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DOI：
10.1109/tac.2004.834121
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发表时间：
2004-09-01
影响因子：
6.8
通讯作者：
Sastry, SS
中科院分区：
文献类型：
--
作者：
Sinopoli, B;Schenato, L;Sastry, SS
受传感器网络内导航和跟踪应用的启发，我们考虑通过间歇性观测执行卡尔曼滤波的问题。当数据在大型无线多跳传感器网络中沿着不可靠的通信通道传输时，控制环路中的通信延迟和信息丢失的影响不容忽视。我们从离散卡尔曼滤波公式开始解决这个问题，并将观测的到达建模为随机过程。我们研究了估计误差协方差的统计收敛特性，表明观测到达率存在一个临界值，超过该临界值就会发生向无界状态误差协方差的转变。我们还给出了预期状态误差协方差的上限和下限。
Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be neglected. We address this problem starting from the discrete Kalman filtering formulation, and modeling the arrival of the observation as a random process. We study the statistical convergence properties of the estimation error covariance, showing the existence of a critical value for the arrival rate of the observations, beyond which a transition to an unbounded state error covariance occurs. We also give upper and lower bounds on this expected state error covariance.