no code implementations • 29 Nov 2023 • Peihu Duan, Tao Liu, Yu Xing, Karl Henrik Johansson
A novel data-driven Kalman filter (DDKF) that combines model identification with state estimation is developed using pre-collected input-output data and uncertain initial state information of the unknown system.
no code implementations • 21 Sep 2023 • Peihu Duan, Yuezu Lv, Guanghui Wen, Maciej Ogorzałek
Further, the proposed method can be applied to pure fully distributed state estimation scenarios and modified for noise-bounded LTI plants.
no code implementations • 15 Jun 2023 • Yunxiao Ren, Zhisheng Duan, Peihu Duan, Ling Shi
The paper presents two main results: a theoretical analysis of the effects of redundant sensors and an engineering-oriented optimal design of redundant sensors.
no code implementations • 24 May 2023 • Peihu Duan, Tao Liu, Yuezu Lv, Guanghui Wen
Cooperative behavior design for multi-agent systems with collective tasks is a critical issue in promoting swarm intelligence.
no code implementations • 15 Mar 2023 • Jiachen Qian, Zhisheng Duan, Peihu Duan, Zhongkui Li
This paper aims to consider the observation problem of periodic systems by bridging two fundamental filtering algorithms for periodic systems with a sensor network: consensus-on-measurement-based distributed filtering (CMDF) and centralized Kalman filtering (CKF).
no code implementations • 21 Nov 2022 • Jiachen Qian, Peihu Duan, Zhisheng Duan, Ling Shi
This paper manages to formulate and investigate a new kind of coupled Riccati equations, called harmonic-coupled Riccati equations (HCRE), from the matrix iterative law of the consensus on information-based distributed filtering (CIDF) algortihm proposed in [1], where the solutions of the equations are coupled with harmonic means.
no code implementations • 14 Jun 2022 • Xiaoxu Lv, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi
This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances.
no code implementations • 18 Jan 2022 • Peihu Duan, Lidong He, Lingying Huang, Guanrong Chen, Ling Shi
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
no code implementations • 13 Dec 2021 • Jiachen Qian, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi
For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering.