面向状态估计的数据驱动功率控制：一种贝叶斯推断方法
Data-Driven Power Control for State Estimation: A Bayesian Inference Approach
AI总结：
本文提出一种基于贝叶斯推断的数据驱动功率控制方法，通过优化传感器传输功率以提升状态估计精度，保留了局部状态估计的高斯性并推导出误差协方差的闭式解。
摘要：
本文考虑利用贝叶斯推断方法进行传感器传输功率控制，以优化状态估计。传感器节点通过不可靠的无线通信信道将局部状态估计发送至远程估计器，传输功率根据局部状态估计的重要性动态调整。所提出的功率控制器被证明能保持局部估计创新的高斯性，从而推导出期望状态估计误差协方差的闭式解。与非数据驱动控制器相比，本文方法在性能上有所提升。
英文摘要：
We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops. As related to packet dropout rate, transmission power is chosen by the sensor based on the relative importance of the local state estimate. The proposed power controller is proved to preserve Gaussianity of local estimate innovation, which enables us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative non data-driven controllers demonstrate performance improvement using our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
