Continuous Estimation Using Context-Dependent Discrete Measurements
Continuous Estimation Using Context-Dependent Discrete Measurements
复制标题
使用上下文相关的离散测量进行连续估计
DOI：
10.1109/tac.2018.2797839
复制
发表时间：
2018
影响因子：
6.8
通讯作者：
Lee, Insup
中科院分区：
文献类型：
--
作者：
Ivanov, Radoslav;Atanasov, Nikolay;Pajic, Miroslav;Weimer, James;Pappas, George J.;Lee, Insup
本文考虑了基于离散上下文的测量的连续状态估计问题。上下文测量提供从系统环境获得的二进制信息，例如指示生命体征高于特定阈值的医疗警报。由于它们提供状态信息，因此这些测量可用于估计目的，类似于标准连续测量，特别是当标准传感器受到偏差或攻击时。假设上下文测量在给定状态下发生的概率是已知的；特别是，我们关注概率函数来对基于阈值的测量进行建模，例如医疗警报场景。我们通过使用具有与真实后验相同的前两个矩的高斯分布来近似后验分布，从而开发了一种递归上下文感知滤波器。我们表明，当接收上下文测量的概率低于所有系统状态的某个正数时，滤波器的预期不确定性是有界的。此外，我们提供了类似可观察性的结果——当且仅当激励条件持续适用于上下文测量时，滤波器协方差矩阵的所有特征值在重复更新后收敛到 0。最后，除了模拟评估之外，我们还将过滤器应用于使用真实患者数据估计手术期间患者血氧含量的问题。
This paper considers the problem of continuous state estimation from discrete context-based measurements. Context measurements provide binary information as obtained from the system's environment, e.g., a medical alarm indicating that a vital sign is above a certain threshold. Since they provide state information, these measurements can be used for estimation purposes, similar to standard continuous measurements, especially when standard sensors are biased or attacked. Context measurements are assumed to have a known probability of occurring given the state; in particular, we focus on the probit function to model threshold-based measurements, such as the medical-alarm scenario. We develop a recursive context-aware filter by approximating the posterior distribution with a Gaussian distribution with the same first two moments as the true posterior. We show that the filter's expected uncertainty is bounded when the probability of receiving context measurements is lower bounded by some positive number for all system states. Furthermore, we provide an observability-like result-all eigenvalues of the filter's covariance matrix converge to 0 after repeated updates if and only if a persistence of excitation condition holds for the context measurements. Finally, in addition to simulation evaluations, we applied the filter to the problem of estimating a patient's blood oxygen content during surgery using real-patient data.