Disturbance noises are always bounded in a practical system, while fusion estimation is to best utilize multiple sensor data containing noises for the purpose of estimating a quantity--a parameter or process. However, few results are focused on the information fusion estimation problem under bounded noises. In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to $\infty$. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.
Distributed Fusion Estimation with Protecting Exogenous Inputs
School of Mathematics and Statistics, Lanzhou University（兰州大学数学与统计学院）；School of Automation and Electrical Engineering, University of Science and Technology Beijing（北京科技大学自动化与电气工程学院）；Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education（教育部工业过程知识自动化重点实验室）；Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统与控制重点实验室）；School of Automation and Electrical Engineering, Zhongyuan University of Technology（中原工学院自动化与电气工程学院）
Optimizing Reweighted Belief Propagation for Distributed Likelihood Fusion Problems
Zenuity AB（Zenuity公司）；ICTEAM Institute/CORE, Université Catholique de Louvain（天主教鲁汶大学ICTEAM研究所/CORE中心）；Electrical Engineering Department, Chalmers University of Technology（查尔姆斯理工大学电气工程系）
A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；University of Salamanca（萨拉曼卡大学）；Institute of Telecommunications, TU Wien（维也纳科技大学电信研究所）；Brno University of Technology（布尔诺理工大学）
Distributed Adaptive Neural Interval Observers for Unknown Nonlinear Systems
Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM)（胡志明市理工大学机械工程学院，越南国立大学胡志明市分校）；Department of Mathematics and Statistics, University of New Mexico（新墨西哥大学数学与统计系）
Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion
School of Sciences, University of Salamanca（萨拉曼卡大学理学院）；Institute of Telecommunications, Vienna University of Technology（维也纳工业大学电信研究所）；School of Mechanical Engineering, Northwestern Polytechnical University（西北工业大学机械工程学院）
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Carleton University（卡尔顿大学）；The Hong Kong Polytechnic University（香港理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Bowling Green State University（鲍灵格林州立大学）
Distributed Output Regulation for a Class of Nonlinear Multi-Agent Systems with Unknown-Input Leaders
School of Automation, Beijing University of Posts and Telecommunications（北京邮电大学自动化学院）；Key Laboratory of Systems and Control, Institute of Systems Science, Chinese Academy of Sciences（中国科学院系统与控制重点实验室，系统科学研究所）
Overlapping Covariance Intersection: Fusion with Partial Structural Knowledge of Correlation from Multiple Sources
Eindhoven University of Technology（埃因霍温理工大学）；Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院系统机器人研究所）
Fusion of finite set distributions: Pointwise consistency and global cardinality
University of Edinburgh（爱丁堡大学）；National University of Singapore（新加坡国立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University College London（伦敦大学学院）；Telecom-SudParis（巴黎电信学院）
