Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of $L_1$ error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirms the effectiveness of the proposed CA-GCI fusion algorithm.
Guaranteed Fast Implementation of the Split Covariance Intersection Filter: Nested Newton Method Thanks to the Fourth-Order Convexity of w-Optimization
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）
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）
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）
RMIT University（皇家墨尔本理工大学）；University of Rochester（罗切斯特大学）；Northwestern Polytechnical University（西北工业大学）；University of Melbourne（墨尔本大学）；Arizona State University（亚利桑那州立大学）；Defence Science and Technology Organisation（国防科学与技术组织）
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）
Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
Department of Mechanical Engineering, Clemson University（机械工程系，克莱姆森大学）
