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State Estimation with Event Sensors: Observability Analysis and Multi-sensor Fusion
SIAM Journal on Control and Optimization ( IF 2.2 ) Pub Date : 2024-01-17 , DOI: 10.1137/22m1539204 Xinhui Liu 1 , Kaikai Zheng 1 , Dawei Shi 2 , Tongwen Chen 3
SIAM Journal on Control and Optimization ( IF 2.2 ) Pub Date : 2024-01-17 , DOI: 10.1137/22m1539204 Xinhui Liu 1 , Kaikai Zheng 1 , Dawei Shi 2 , Tongwen Chen 3
Affiliation
SIAM Journal on Control and Optimization, Volume 62, Issue 1, Page 167-190, February 2024.
Abstract. This work investigates a state estimation problem for linear time-invariant systems based on polarized measurement information from event sensors. To enable estimator design, a new notion of observability, namely, [math]-observability is defined with the precision parameter [math] which relates to the worst-case performance of inferring the initial state, based on which a criterion is developed to test the [math]-observability of discrete-time linear systems. Utilizing multisensor polarity data from event sensors and the implicit information hidden in event-triggering conditions at no-event instants, an iterative event-triggered state estimator is designed to evaluate a set containing all possible values of the state. The proposed estimator is built by outer approximation of intersecting ellipsoids that are predicted from previous state estimates and the ellipsoids inferred from received polarity information of event sensors as well as the event-triggering protocol; the estimated regions of the state derived from multisensor event measurements are fused together, the sizes of which are proved to be asymptotically bounded. Distributed implementation of the estimation algorithm utilizing a two-layer processor network of hierarchy architecture is discussed, and the temporal computational complexity of the algorithm implemented in centralized and distributed ways is analyzed. The efficiency of the proposed event-triggered state estimator is verified by numerical experiments.
中文翻译：
使用事件传感器进行状态估计：可观测性分析和多传感器融合
SIAM 控制与优化杂志，第 62 卷，第 1 期，第 167-190 页，2024 年 2 月。
摘要。这项工作研究了基于事件传感器的偏振测量信息的线性时不变系统的状态估计问题。为了实现估计器设计，使用精度参数[math]定义了新的可观测性概念，即[math]-可观测性，该精度参数与推断初始状态的最坏情况性能相关，并在此基础上开发了一个标准来测试离散时间线性系统的[数学]可观测性。利用来自事件传感器的多传感器极性数据和隐藏在无事件时刻的事件触发条件中的隐式信息，设计迭代事件触发状态估计器来评估包含状态的所有可能值的集合。所提出的估计器是通过从先前的状态估计预测的相交椭球体的外近似和从接收到的事件传感器的极性信息以及事件触发协议推断的椭球体构建的；从多传感器事件测量得出的状态估计区域融合在一起，其大小被证明是渐近有界的。讨论了利用层次结构的两层处理器网络的估计算法的分布式实现，并分析了集中式和分布式实现的算法的时间计算复杂度。通过数值实验验证了所提出的事件触发状态估计器的效率。
更新日期：2024-01-18
Abstract. This work investigates a state estimation problem for linear time-invariant systems based on polarized measurement information from event sensors. To enable estimator design, a new notion of observability, namely, [math]-observability is defined with the precision parameter [math] which relates to the worst-case performance of inferring the initial state, based on which a criterion is developed to test the [math]-observability of discrete-time linear systems. Utilizing multisensor polarity data from event sensors and the implicit information hidden in event-triggering conditions at no-event instants, an iterative event-triggered state estimator is designed to evaluate a set containing all possible values of the state. The proposed estimator is built by outer approximation of intersecting ellipsoids that are predicted from previous state estimates and the ellipsoids inferred from received polarity information of event sensors as well as the event-triggering protocol; the estimated regions of the state derived from multisensor event measurements are fused together, the sizes of which are proved to be asymptotically bounded. Distributed implementation of the estimation algorithm utilizing a two-layer processor network of hierarchy architecture is discussed, and the temporal computational complexity of the algorithm implemented in centralized and distributed ways is analyzed. The efficiency of the proposed event-triggered state estimator is verified by numerical experiments.
中文翻译：
使用事件传感器进行状态估计：可观测性分析和多传感器融合
SIAM 控制与优化杂志，第 62 卷，第 1 期，第 167-190 页，2024 年 2 月。
摘要。这项工作研究了基于事件传感器的偏振测量信息的线性时不变系统的状态估计问题。为了实现估计器设计，使用精度参数[math]定义了新的可观测性概念，即[math]-可观测性，该精度参数与推断初始状态的最坏情况性能相关，并在此基础上开发了一个标准来测试离散时间线性系统的[数学]可观测性。利用来自事件传感器的多传感器极性数据和隐藏在无事件时刻的事件触发条件中的隐式信息，设计迭代事件触发状态估计器来评估包含状态的所有可能值的集合。所提出的估计器是通过从先前的状态估计预测的相交椭球体的外近似和从接收到的事件传感器的极性信息以及事件触发协议推断的椭球体构建的；从多传感器事件测量得出的状态估计区域融合在一起，其大小被证明是渐近有界的。讨论了利用层次结构的两层处理器网络的估计算法的分布式实现，并分析了集中式和分布式实现的算法的时间计算复杂度。通过数值实验验证了所提出的事件触发状态估计器的效率。
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