This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event-trigger is developed to handle network-induced packet dropouts as well as packet disorders resulting from random transmission delays, where the ${H_2}/{H_\infty }$ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complexity network-induced problem, which may deteriorate the system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.
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. However, due to the limited communication resources in physical systems, the number of fusion steps cannot be infinite. To deal with this issue, the present paper analyzes the performance of CMDF with finite consensus fusion operations. First, by introducing a modified discrete-time algebraic Riccati equation and several novel techniques, the convergence …
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）
Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulat…
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）
This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart sensor-to-fusion center channel), an event-triggered strategy and a dimensionality reduction strategy are introduced in a unified networked framework to lighten the communication burden. Then, two kinds of compensation strategies in terms of a unified model are designed to restr…
A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited field-of-view (FoV) of any individual agent by successfully exploiting cooperation among multi-view agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel principled information fusion approach for dealing with multi-view multi-agent case, on the basis of Generalized Covariance Intersection (GCI). The proposed method can be used to perform multi-object tr…
MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation
Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；School of Automation Engineering, University of Electronic Science and Technology of China（电子科技大学自动化工程学院）
This paper deals with solving distributed optimization problems with equality constraints by a class of uncertain nonlinear heterogeneous dynamic multi-agent systems. It is assumed that each agent with an uncertain dynamic model has limited information about the main problem and limited access to the information of the state variables of the other agents. A distributed algorithm that guarantees cooperative solving of the constrained optimization problem by the agents is proposed. Via this algorithm, the agents do not need to continuously broadcast their data. It is shown that the proposed algo…
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）
