The Observability in Unobservable Systems

VINS与LINS的局部可观测性

Local Observability of VINS and LINS

本文分析VINS和LINS非线性模型的不可观测方向，明确二者不可观测方向一致（全局平移与绕重力矢量的全局旋转），仅所需观测特征点数量不同，并证明了OC-VINS中的一项约束。

State Estimation in Unobservable Power Systems via Graph Signal Processing Tools

We consider the problem of estimating the states in an unobservable power system. To this end, we propose novel graph signal processing (GSP) methods. For simplicity, we start with analyzing the DC power flow (DC-PF) model and then extend our algorithms to the AC power flow (AC-PF) model. The main assumption behind the proposed GSP approach is that the grid states, which include the vector of phases and the vector of the magnitudes of the voltages in the system, is a smooth graph signal with respect to the system admittance matrix that represents the underlying graph. Thus, the first step in t…

在传感器网络上具有全局可观测性的分布式状态估计一致性

Consistent distributed state estimation with global observability over sensor network

本文研究了一类离散时间变系统在传感器网络上的分布式状态估计问题，提出了一种子最优分布式卡尔曼滤波器，证明其一致性并设计自适应CI权重，同时基于强连通性和全局可观测性证明了协方差有界性和滤波收敛性。

基于现场PMU数据的实际输电系统机器学习状态估计

Machine Learning-Based State Estimation for an Actual Transmission System Using Field PMU Data

本文利用美国实际电网数据，评估深度神经网络在PMU不可观测输电系统中进行时间同步状态估计的可行性，系统分析其精度、可扩展性与计算性能。

Functional observability and target state estimation in large-scale networks

The quantitative understanding and precise control of complex dynamical systems can only be achieved by observing their internal states via measurement and/or estimation. In large-scale dynamical networks, it is often difficult or physically impossible to have enough sensor nodes to make the system fully observable. Even if the system is in principle observable, high-dimensionality poses fundamental limits on the computational tractability and performance of a full-state observer. To overcome the curse of dimensionality, we instead require the system to be functionally observable, meaning that…

数据驱动的复杂网络功能状态估计

Data-driven functional state estimation of complex networks

本文提出了一种数据驱动的方法，用于估计复杂网络中目标状态变量，无需模型参数，适用于不可观测系统，并展示了其在多个应用中的有效性。