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首页> 外文期刊>IEEE Transactions on Automatic Control >Distributed Kalman Filtering Over Wireless Sensor Networks in the Presence of Data Packet Drops
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Distributed Kalman Filtering Over Wireless Sensor Networks in the Presence of Data Packet Drops

机译：在存在数据包丢弃的情况下，分布式卡尔曼过滤通过无线传感器网络

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摘要

We study distributed Kalman filtering over the wireless sensor network, where each sensor node is required to locally estimate the state of a linear time-invariant discrete-time system. using its own observations and those transmitted from its neighbors in the presence of data packet drops. This is an optimal one-step prediction problem under the framework of distributed estimation, assuming the TCP-like protocol. We first present the stationary distributed Kalman filter (DKF) that minimizes the local average error variance in the steady state at each sensor node, based on the stabilizing solution to the corresponding modified algebraic Riccati equation (MARE). The existence of the stabilizing solution to the MARE is addressed by adopting the stability margin, which can be computed by solving a set of linear matrix inequalities. Then, the Kalman consensus filter (KCF), consisting of the stationary DKF and a consensus term of prior estimates, is studied. Finally, the performance of the stationary DKF and KCF is illustrated by a numerical example.
机译：我们研究了无线传感器网络上的分布式卡尔曼滤波，其中每个传感器节点都需要在本地估计线性时间不变离散时间系统的状态。使用自己的观察和从其邻居发送的观察结果在数据包下降的存在下。这是在分布式估计框架下的最佳一步预测问题，假设TCP类似的协议。我们首先介绍静止分布式卡尔曼滤波器（DKF），其基于对应改进的代数Riccati等式（MARE）的稳定解决方案，最小化每个传感器节点处的稳态的局部平均误差方差。通过采用稳定性余量来解决母马对母马的稳定溶液的存在，这可以通过求解一组线性矩阵不等式来计算。然后，研究了由静止的DKF和先前估计的共识期间的卡尔曼共识滤波器（KCF）。最后，通过数值示例说明了静止DKF和KCF的性能。

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