State estimation incorporating infrequent, delayed and integral measurements

Guo, Yafeng; Huang, Biao

doi:10.1016/j.automatica.2015.05.001

Cited by 65 publications

(23 citation statements)

References 18 publications

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“…3) Estimation Performance: The mean root-mean-square error (MRMSE), which is often used in the literature for nonlinear state estimation ( [36] and references therein), was used to obtain the accuracy of the resulting state estimates by comparing the estimated field with the generated field. The MRMSE is defined at each time instant k as…”

Section: ) Basis Function Selectionmentioning

confidence: 99%

A Spatiotemporal Estimation Framework for Real-World LIDAR Wind Speed Measurements

Mercieca

Aram

Jones

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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Section: ) Basis Function Selectionmentioning

confidence: 99%

A Spatiotemporal Estimation Framework for Real-World LIDAR Wind Speed Measurements

Mercieca

Aram

Jones

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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“…Finally, F ¯ i k in Assumption 4 is close related to the reversibility of this system. Those assumptions are quite standard in the stability analysis literature of EKF or sigma point-based algorithms (Battistelli and Chisci, 2014b, 2016; Guo and Huang, 2015; Li et al, 2016; Reif et al, 2004).…”

Section: Stability Analysismentioning

confidence: 99%

Hybrid consensus sigma point approximation nonlinear filter using statistical linearization

Wang

Zhang

2018

Transactions of the Institute of Measurement and Control

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In this paper, a hybrid consensus sigma point approximation nonlinear filter is proposed for state estimation in collaborative sensor network, where hybrid consensus of both measurement and information is utilised. Statistical linearization of nonlinear functions is used in sigma point filters, that is, unscented Kalman filter (UKF), cubature Kalman filter (CKF), and central difference Kalman filter (CDKF). Stability of the proposed algorithm is also analysed with the help of linearization operation and some conservative assumptions. Two typical target tracking examples are used to demonstrate the effectiveness of the proposed algorithms. Simulation results show that the proposed algorithms are more stable than existing algorithms, and among our proposed algorithms, CKF- and CDKF-based algorithms are more accurate and stable than the UKF-based one.

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“…The above phenomena are called the integral measurements, which often emerges in engineering applications like nuclear reaction processes (Casoli et al, 2014;Markov & Mikhailov, 2014;Tripathi et al, 2011) and chemical reaction processes (Guo & Huang, 2015). In light of its universality, the state estimate problem regarding the integral measurements has received initial attention recently (Guo & Huang, 2015;Liu et al, 2018;Shen et al, 2020). The H ∞ state estimation problem has been investigated in Shen et al (2020) for multi-rate artificial neural networks with integral measurements.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered distributed filtering for discrete-time systems with integral measurements

Cong

Han

et al. 2021

Systems Science & Control Engineering

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This paper handles the event-triggered distributed filtering problem for a class of discrete-time systems with integral measurements over sensor networks (SNs). First of all, a plant is monitored by an SN formulated by a digraph. The integral measurement phenomenon is modelled for each node to account for the interval time taken by sample collection. Next, to mitigate the communication burden of network, an event generator function is introduced for each node to regulate the data transmission to its neighbouring nodes. This paper aims to find the suitable filter gains for each node such that the filtering error dynamics satisfies the exponentially ultimately boundedness. Via the Lyapunov stability theory, a sufficient condition is established which ensures the existence of the distributed filter under the desirable performance index. Moreover, the ultimate bound in the performance index is minimized via the robust optimization method. Then, the filter gains are acquired by solving a linear matrix inequality with the YALMIP toolbox. Finally, a numerical simulation example is employed to verify the usefulness of the distributed filtering algorithm developed in this paper.

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State estimation incorporating infrequent, delayed and integral measurements

Cited by 65 publications

References 18 publications

A Spatiotemporal Estimation Framework for Real-World LIDAR Wind Speed Measurements

A Spatiotemporal Estimation Framework for Real-World LIDAR Wind Speed Measurements

Hybrid consensus sigma point approximation nonlinear filter using statistical linearization

Event-triggered distributed filtering for discrete-time systems with integral measurements

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