This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $δ$-GLMB, marginalized $δ$-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.
This paper addresses fusion of labeled random finite set (LRFS) densities according to the criterion of minimum information loss (MIL). The MIL criterion amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs) with the fused density appearing as righthand argument of the KLDs. In order to ensure the fused density to be consistent with the local ones when LRFS densities are marginal $δ$-generalized labeled multi-Bernoulli (M$δ$-GLMB) or labeled multi-Bernoulli (LMB) densities, the MIL rule is further elaborated by imposing the constraint that the fused density be in the …
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. More…
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）
This paper proposes a computationally efficient algorithm for distributed fusion in a sensor network in which multi-Bernoulli (MB) filters are locally running in every sensor node for multi-target tracking. The generalized Covariance Intersection (GCI) fusion rule is employed to fuse multiple MB random finite set densities. The fused density comprises a set of fusion hypotheses that grow exponentially with the number of Bernoulli components. Thus, GCI fusion with MB filters can become computationally intractable in practical applications that involve tracking of even a moderate number of objec…
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…
This paper addresses distributed multi-target tracking (DMTT) over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinality probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has a limited FoV, the commonly adopted fusion methods become unreliable. In fact, the monitored area of multiple sensor nodes consists of several parts that are either exclusive of a single node, i.e. exclusive FoVs (eFoVs) or common to multiple (at least two) nodes, i.e. common FoVs (cFoVs). In this setting, the crucial issue is …
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）
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 estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean square estimation error at each node (accuracy objective), and ii) the mean square distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing th…
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）
