It is known that polynomial filtering can accelerate the convergence towards average consensus on an undirected network. In this paper the gain of a second-order filtering is investigated. A set of graphs is determined for which consensus can be attained in finite time, and a preconditioner is proposed to adapt the undirected weights of any given graph to achieve fastest convergence with the polynomial filter. The corresponding cost function differs from the traditional spectral gap, as it favors grouping the eigenvalues in two clusters. A possible loss of robustness of the polynomial filter is also highlighted.
Stochastic Average Consensus Filtering and Distributed State Estimation for Boolean Control Networks
School of Mathematics, Southwestern University of Finance and Economics（西南财经大学数学学院）；School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics（西南财经大学计算机与人工智能学院）
Distributed Kalman filtering with minimum-time consensus algorithm
Huazhong University of Science and Technology（华中科技大学）；The Hong Kong University of Science and Technology（香港科技大学）；University of Waterloo（滑铁卢大学）；The University of Newcastle（纽卡斯尔大学）；University of Cambridge（剑桥大学）；University of Luxembourg, Faculté des Sciences, de la Technologie et de la Communication（卢森堡大学科学与技术通讯学院）
Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation
Department of Mechanical and Mechatronics Engineering, University of Waterloo（滑铁卢大学机械与机电工程系）；Department of Mechanical Engineering, University of Ottawa（渥太华大学机械工程系）
Distributed Kalman Filters with State Equality Constraints: Time-based and Event-triggered Communications
KTH Royal Institute of Technology（皇家理工学院）；Key Laboratory of Systems and Control, 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）；Rocket Force University of Engineering（火箭军工程大学）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（香港科技大学电子及计算机工程系）
