Optimal Sensor Scheduling for Remote State Estimation Over Hidden Markovian Channels

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS
Bowen Sun;Xianghui Cao
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引用次数: 0

Abstract

This letter investigates the sensor scheduling problem in a multi-sensor, multi-channel remote state estimation system with time-varying channel states. Due to channel constraints, only a subset of sensors can transmit data to the remote state estimator at each time step. To prevent packet loss when transmitting when the channel state is busy, channel sensing is performed before transmission; however, sensing results are unreliable in practice. To address these challenges, we propose a channel state estimation algorithm using a Hidden Markov Model (HMM) to estimate the true channel state. We then formulate the sensor transmission scheduling problem as a Markov decision process (MDP) and prove the existence of a deterministic, stationary optimal policy. Additionally, we show that the optimal policy follows a monotone structure. Numerical examples are presented to illustrate the effectiveness of the proposed method.
隐马尔可夫信道上远程状态估计的最优传感器调度
本文研究了具有时变信道状态的多传感器、多通道远程状态估计系统中的传感器调度问题。由于信道的限制,每个时间步长只有一部分传感器可以向远程状态估计器传输数据。为了防止在信道状态为忙时传输时丢包,在传输前进行信道感知;然而,在实际应用中,传感结果并不可靠。为了解决这些问题,我们提出了一种使用隐马尔可夫模型(HMM)估计信道真实状态的信道状态估计算法。然后,我们将传感器传输调度问题表述为马尔可夫决策过程,并证明了一个确定性、平稳的最优策略的存在性。此外,我们还证明了最优策略遵循单调结构。数值算例说明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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