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distributed data fusion for On-Scene Signal Sensing With a Multi-UAV System

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第3期7卷 1330-1341页

作者： Du, Bin Mao, Ruijiu Kong, Nan Sun, Dengfeng Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA Purdue Univ Weldon Sch Biomed Engn W Lafayette IN 47906 USA

This article presents an algorithmic framework for distributed data fusion using a network of multiple unmanned aerial vehicles (UAVs). Each UAV in the network is capable of sensing signals, performing computational tasks, and exchanging information with its immediate neighbors. Based on the limited local measurement and exchanged information only from neighbors, we aim to propose a fully distributed algorithm to integrate information from various measurements of individual UAVs. As an important building block of the distributed data fusion algorithm, a novel sum consensus scheme is also developed with only assuming row-stochastic weight matrices. Consequently, through the proposed distributed algorithm, each UAV is able to obtain a more reliable fused sensing result with higher accuracy, compared to the local measurement collected by the UAV itself. The effectiveness of the proposed algorithmic framework is validated by both theoretical analysis and numerical simulations.

关键词： distributed data fusion distributed power method multi-UAV system optimal averaging sum consensus

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distributed data fusion for real-time crowding estimation

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SIGNAL PROCESSING 1996年第1期53卷 47-63页

作者： Regazzoni, CS Tesei, A UNIV GENOA DEPT BIOPHYS & ELECT ENGN DIBE I-16145 GENOA ITALY

This paper describes an approach to estimating the number of pedestrians present in a real-life complex scene by processing a sequence of images. A set of features extracted from each acquired image is related to the number of people monitored. The static declarative knowledge describing the correlation between a feature value and the estimate of the number of people is represented by nonlinear piecewise models. The proposed estimation approach exploits temporal information by means of a distributed Kalman filter network. The current crowding estimate is derived from the application of the static correlation models to the extracted observations (evidence), combined with the prediction obtained from the temporally previous estimate (expectation). This approach improves the estimation accuracy obtained by using only current available data (i.e., static knowledge), and can predict crowding values between two successive acquisitions, i.e., at those steps at which new data cannot be available. Extensive experimental tests carried out in the field (a metro station) yielded very promising results in terms of estimation accuracy and real-time response capability.

关键词： crowding estimation distributed data fusion distributed extended Kalman filtering nonlinear static and dynamic knowledge modelling

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distributed data fusion over an ad hoc network

Distributed data fusion over an ad hoc network

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7th Battlespace Digitization and Network-Centric Warfare Conference

作者： Anderson, S Binns, L Collins, P Cooke, A Greenway, P Valachis, D Univ. of Sheffield (United Kingdom) BAE SYSTEMS (United Kingdom) Univ. of Manchester Institute of Science and Tech (United Kingdom)

ISBN: (纸本)081944491X

We have been developing a decentralised architecture for data fusion for several years. In this architecture, sensing nodes, each with their own processing, are networked together. Previously, we have researched fully connected networks, tree-connected networks, and networks with loops, and have developed a range of theoretical and empirical results for dynamic networks. Here we report the results obtained from building and demonstrating a decentralised data fusion system in which the nodes are connected via an ad hoc network. Several vision based tracking nodes are linked via a wireless LAN. We use UDP to establish local routing tables within the network whenever a node joins, and TCP/IP to provide point to point communications within the network. We show that the resulting data fusion system is modular, scalable and fault tolerant. In particular, we demonstrate robustness to nodes joining and leaving the network, either by choice or as a result of link drop-out. In addition to experimental results from the project, we present some thoughts on how the technology could be applied to large scale, heterogeneous sensor networks.

关键词： distributed data fusion Adhoc networks

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distributed data fusion USING ITERATIVE COVARIANCE INTERSECTION

DISTRIBUTED DATA FUSION USING ITERATIVE COVARIANCE INTERSECT...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Hlinka, Ondrej Sluciak, Ondrej Hlawatsch, Franz Rupp, Markus Vienna Univ Technol Inst Telecommun Vienna Austria

ISBN: (纸本)9781479928934

We propose an iterative extension of the covariance intersection (CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to a consensus across all network nodes. We furthermore apply the ICI algorithm to distributed sequential Bayesian estimation and propose an ICI-based distributed particle filter (DPF). This DPF allows for spatially correlated measurement noises with unknown cross-correlations and does not require knowledge of the network size. The performance of the proposed DPF is assessed experimentally for a target tracking problem.

关键词： distributed data fusion covariance intersection distributed estimation distributed particle filter sensor network

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Privacy-Preserving distributed data fusion Based on Attribute Protection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第10期15卷 5765-5777页

作者： Su, Xin Fan, Kuan Shi, Wenbo Hohai Univ Coll Internet Things Engn Changzhou 213022 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China

Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.

关键词： data as a server (DaaS) distributed data fusion differential privacy K-anonymous privacy preserving

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A case study with design of experiments: Performance evaluation methodology for Level 1 distributed data fusion processes

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INFORMATION fusion 2011年第2期12卷 93-104页

作者： Sambhoos, Kedar Bowman, Christopher Llinas, James CUBRC Buffalo NY 14225 USA Data Fus & Neural Networks Broomfield CO USA SUNY Buffalo Ctr Multisource Informat Fus Buffalo NY 14260 USA

The emphasis of this paper is to design a performance evaluation methodology for Level 1 distributed data fusion processes. However, there is little empirical research been done so far to define a rigorous, technically fair, yet affordable method for performance evaluation for data fusion processes. Within this methodology Performance Evaluation process is treated as a completely new and different fusion process. Here we address the distributed Level 1 fusion problem and give quantitative insights into the interdependencies and the consistency measures between distributed fusion measures of performance. Based on our prior research, our suggested performance evaluation methodology is based upon the Dual Node Network data fusion 82 Resource Management Architecture. Our case study involves track picture consistency across multiple airborne platforms and sensors for what we label as Tier 0, Tier 1 and Tier 2 Level 1 fusion (i.e., entity or object assessment). The highlight of the paper is a proposed approach for an overarching performance evaluation methodology for distributed Level 1 fusion that is meticulous, accounts for the complexities of the "Track-to-Truth" association problems, and permits the effects and interactions of various independent variables (factors) to be analyzed. This research also focuses on analyzing the measures of performances by setting up Design of Experiments. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Performance evaluation distributed data fusion Dual Node Network Architecture

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LPPTE: A lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications

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INFORMATION fusion 2021年 73卷 144-156页

作者： Liu, Zhiquan Ma, Jianfeng Weng, Jian Huang, Feiran Wu, Yongdong Wei, Linfeng Li, Yuxian Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China

Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects.

关键词： Vehicular networks Cooperative vehicular safety applications Trust evaluation Privacy preservation Overhead distributed data fusion

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D2F: A Routing Protocol for distributed data fusion in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2013年第1期70卷 391-410页

作者： Lu, Zongqing Tan, Su-Lim Biswas, Jit Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Inst Infocomm Res Singapore Singapore

data fusion can be distributed into network and executed on network nodes, to reduce data from redundant sensor nodes, to fuse the information from complementary sensor nodes and to get the complete view from cooperative nodes. Consequently only the inference of interest is sent to end user. This distributed data fusion can significantly reduce the data transmission cost and there is no need for a powerful centralized node to process the collected information. However, to achieve the advantages of distributed data fusion and better utilization of network resources, each fusion function needs to be performed at particular network node for minimizing energy cost of data fusion application, both data transmission cost and computation cost. In this paper, distributed data fusion routing (D2F) is proposed, which is designed for deploying distributed data fusion application in wireless sensor networks. D2F can find the optimal route path and fusion placements for a given data fusion tree, which obtains the optimal energy consumption for in-network data fusion. D2F can also handle different link failures and maintain the optimality of energy cost of data fusion by adapting to the dynamic change of network.

关键词： Wireless sensor networks distributed data fusion Routing protocol fusion placement

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Detecting Road Events Using distributed data fusion: Experimental Evaluation for the Icy Roads Case

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第1期17卷 184-194页

作者： Radak, Jovan Ducourthial, Bertrand Cherfaoui, Veronique Bonnet, Stephane Univ Technol Compiegne Univ Paris 04 CNRS HeudiasycCtr Rech Royallieu F-60203 Compiegne France

One of the main ideas in the area of intelligent transport systems is to use all possible information coming from vehicles and infrastructure, in order to make the system "smarter" and avoid potentially dangerous situations-collisions, accidents, bottleneck, etc. However, data are sometimes unreliable due to source and communication network quality, leading vehicles or even the whole system to wrong decisions. We present a generic method for detecting dangerous events on the road. To support unreliable data sources, it uses distributed data fusion. Moreover, to deal with network failures, it relies on a self-stabilizing generic distributed algorithm. Our method mixes measurements obtained from vehicle onboard sensors, as well as wireless sensors placed close to the road and connected to road side units. Each vehicle computes how confident it is about a potential dangerous event using both local and remote data. To evaluate our approach, we implemented it using a specific hardware and software platform. Moreover, we instantiated a simple, yet efficient application to detect icy roads, based on temperature measurements. Thanks to both in-lab and actual on-the-road experiments, we demonstrate the possibility to deduce proper results from unreliable data and, consequently, the correctness and usefulness of our approach.

关键词： Event detection distributed data fusion belief function VANET V2V and V2I communication road experiment

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Information Flow Control for Collaborative distributed data fusion and Multisensor Multitarget Tracking

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2012年第4期42卷 501-517页

作者： Akselrod, David Sinha, Abhijit Kirubarajan, Thiagalingam McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

Decentralized multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms, is presented. A decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system is presented as well. A distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable is considered. In this study, the approach to use an information-based objective function is utilized. The objective function is based on the posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes that are used to control the data-fusion process. Three distributed data-fusion algorithms-associated measurement fusion, tracklet fusion, and track-to-track fusion-are analyzed. This paper also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.

关键词： distributed data fusion Fisher information measure (FIM) information flow control Markov decision process (MDP) multitarget multisensor tracking posterior Cramer-Rao lower bound (PCRLB)

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