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检索条件"主题词=distributed data fusion"

共 56 条记录，以下是31-40 订阅

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A Joint Perception Scheme For Connected Vehicles

A Joint Perception Scheme For Connected Vehicles

引用

IEEE Sensors Conference

作者： Ahmed, Ahmed N. Ravijts, Ian de Hoog, Jens Anwar, Ali Mercelis, Siegfried Hellinckx, Peter Univ Antwerp Dept Elect & ICT Engn IDLab Imec Antwerp Belgium AP Univ Appl Sci Dept Appl Informat Antwerp Belgium

ISBN: (纸本)9781665484640

Currently visual sensing systems, used in autonomous vehicle's research, typically perceive the surrounding environment up to 250m ahead of the vehicle. However, the detection reliability drops when the object's position is more than 50m, due to objects being sparse or unclear for the detection model to make a confident detection. Cooperative perception extends the visual horizon of the onboard sensing system, by expanding the sensing range which improves the detection precision. This paper explores early distributed visual data fusion by creating a multi-vehicle dataset using the Carla simulator to create a shared driving scenario, equipping every spawned vehicle with LiDAR, GNSS, and IMU sensors to emulate a real-driving scenario. Furthermore, we investigate the usage of ZeroMQ-based communication system to distribute visual and meta- data across relevant neighboring vehicles. Since the proposed method distributes raw LiDAR data, we utilize point cloud compression to reduce the size of the published data between relevant connected vehicles to satisfy communication bandwidth requirements. Subsequently, we transform and fuse the received data, and apply a deep learning object detection model to detect the objects in the scene. Our experiments prove that our proposed framework improves the detection average precision while satisfying bandwidth requirements.

关键词： point clouds shared situational awareness connected vehicles distributed data fusion

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The challenge of scalable and distributed fusion of disparate sources of information

The challenge of scalable and distributed fusion of disparat...

引用

Conference on Multisensor, Multisource Information fusion

作者： Julier, Simon J. Uhlmann, Jeffrey K. Walters, Joshua Mittu, Ranjeev Palaniappan, Kannappan ITT AES NRL 4555 Overlook Ave Washington DC 20375 USA Univ Missouri Dept Comp Sci Columbia MO 65211 USA Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)0819462985

A key enabler for Network Centric Warfare (NCW) is a sensor network that can collect and fuse vast amounts of disparate and complementary information from sensors that are geographically dispersed throughout the battlespace. This information will lead to better situation awareness so that commanders will be able to act faster and more effectively. However, these benefits are possible only if the sensor data can be fused and synthesized for distribution to the right user in the right form at the right time within the constraints of available bandwidth. In this paper we consider the problem of developing Level 1 data fusion algorithms for disparate fusion in NCW. These algorithms must be capable of operating in a fully distributed (or decentralized) manner;must be able to scale to extremely large numbers of entities;and must be able to combine many disparate types of data. To meet these needs we propose a framework that consists of three main components: an attribute-based state representation that treats an entity state as a collection of attributes, new methods or interpretations of uncertainty, and robust algorithms for distributed data fusion. We illustrate the discussion in the context of maritime domain awareness, mobile adhoc networks, and multispectral image fusion.

关键词： distributed data fusion disparate data fusion generalized Covariance Intersection correlated data MANET maritime domain awareness

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Kalman-Consensus Filter : Optimality, Stability, and Performance

Kalman-Consensus Filter : Optimality, Stability, and Perform...

引用

Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)

作者： Olfati-Saber, Reza Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

ISBN: (纸本)9781424438723

One of the fundamental problems in sensor networks is to estimate and track the state of targets (or dynamic processes) of interest that evolve in the sensing field. Kalman filtering has been an effective algorithm for tracking dynamic processes for over four decades. distributed Kalman Filtering (DKF) involves design of the information processing algorithm of a network of estimator agents with a two-fold objective: 1) estimate the state of the target of interest and 2) reach a consensus with neighboring estimator agents on the state estimate. We refer to this DKF algorithm as Kalman-Consensus Filter (KCF). The main contributions of this paper are as follows: i) finding the optimal decentralized Kalman-Consensus filter and showing that its computational and communication costs are not scalable in n and ii) introducing a scalable suboptimal Kalman-Consensus Filter and providing a formal stability and performance analysis of this distributed and cooperative filtering algorithm. Kalman-Consensus Filtering algorithm is applicable to sensor networks with variable topology including mobile sensor networks and networks with packet-loss.

关键词： sensor networks Kalman-Consensus filtering distributed Kalman filtering distributed data fusion

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Advanced algorithms for distributed fusion

Advanced algorithms for distributed fusion

引用

Conference on Multisensor, Multisource Information fusion

作者： Gelfand, A. Smith, C. Colony, M. Bowman, C. Pei, R. Huynh, T. Brown, C. Decis Analyt Corp 1235 S Clark StSuite 400 Arlington VA 22202 USA Data Fus Neural Networks Broomfield CO 80020 USA US Army CERDEC Intelligence & Informat Warfare Directorat Ft Monmouth NJ USA

ISBN: (纸本)9780819471659

The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.

关键词： distributed data fusion decentralized data fusion consistency assessment performance evaluation resource management Dual Node Network (DNN)

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Conservative Compression of Information Matrices using Event-Triggering and Robust Optimization 27

Conservative Compression of Information Matrices using Event...

引用

27th International Conference on Information fusion (fusion)

作者： Funk, Christopher Noack, Benjamin Otto von Guericke Univ Inst Intelligent Cooperating Syst Autonomous Multisensor Syst Grp Magdeburg Germany

ISBN: (纸本)9798350371420;9781737749769

distributed sensor fusion requires the transmission of intermediate fusion results, consisting of point estimates and associated error covariance or information matrices. Bandwidth constraints necessitate data compression techniques for error covariance and information matrices, which typically dominate data volume. To ensure the safe use of the fusion results for decision-making, these techniques must be conservative, i.e., not lead to the compressed error covariance or information matrices underestimating the true estimate error. This work introduces a novel approach for the conservative compressed transmission of information matrices, that builds on a previous event-based method for covariance matrices. The proposed method allows the entire sensor fusion pipeline to operate in 'information space', facilitating efficient fusion operations without the need to compute corresponding covariance matrices. Contributions include an event-trigger for information matrices and a robust-optimization-based bounding mechanism ensuring conservativeness. The proposed approach is evaluated in the context of transmitting error information matrices generated by extended information filter SLAM to a receiver for further processing.

关键词： distributed data fusion data Reduction Conservative data fusion

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distributed tracking in sensor networks with limited sensing range

Distributed tracking in sensor networks with limited sensing...

引用

American Control Conference 2008

作者： Olfati-Saber, Reza Sandell, Nils F. Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9781424420780

In this paper, we address the problem of distributed tracking of a maneuvering target using sensor networks with nodes that possess limited sensing range (LSR). In such sensor networks, a target can only be observed by a small percentage of the sensors and is practically hidden to the remaining majority of the nodes. This feature is shared among most of today's wireless sensor networks and differentiates them from their traditional counterparts involving data fusion for long-range sensors such as radars and sonars. distributed Kalman filters have proven to be effective and scalable algorithms for distributed tracking in sensor networks. Our main contribution is to give a message-passing version of the Kalman-Consensus Filter (KCF)-introduced by the first author in CDC '07-that is capable of distributed tracking of a maneuvering target with a satisfactory performance. The architecture of this filter is a peer-to-peer (P2P) network of microfilters as extensions of local Kalman filters. The model proposed for the maneuvering target is a piece-wise linear switching system with two distinct modes of behavior that enables the target to stay inside a rectangular region in all time (for a bounded set of initial conditions). Simulation results are provided for a lattice-type sensor network with 100 LSR nodes tracking a target with switching modes of behavior which demonstrate the effectiveness of the proposed distributed data fusion and tracking algorithms.

关键词： sensor networks Kalman-Consensus filtering distributed data fusion target tracking

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Centralized multi-sensor multi-target data fusion with tracks as measurements 32

Centralized multi-sensor multi-target data fusion with track...

引用

Conference on Signal Processing, Sensor/Information fusion, and Target Recognition XXXII

作者： Hunter, Emily Clark, Daniel E. Balaji, Bhashyam O'Rourke, Sean Queens Univ Sch Comp Kingston ON Canada Univ Southampton Elect & Comp Sci Southampton Hants England Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Air Force Res Lab Sensors Directorate Dayton OH USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker;(ii) modify multi-target trackers to take a set of tracks as a set of measurements;and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.

关键词： Covariance intersection multi-target tracking multi-sensor fusion distributed data fusion

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Consensus Sigma-Point Information Filter for Large-Scale Sensor Networks

Consensus Sigma-Point Information Filter for Large-Scale Sen...

引用

9th International Symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES 2010)

作者： Kim, Du Yong Yoon, Ju Hong Shin, Vladimir Gwangju Inst Sci & Technol Dept Mechatron Kwangju South Korea

ISBN: (纸本)9780769541105

We address an estimation problem of nonlinear dynamic system through a large-scale sensor network. Even though much research has been done in data fusion, the extension to nonlinear dynamic system is recently focused. The main difficulty in data fusion of nonlinear dynamic system comes from that effective nonlinear filters do not allow the information form. In this paper, two algorithms are considered to implement distributed Kalman filtering for a large-scale sensor network. data fusion problem for a large-scale sensor network is tackled by using Kalman-Consensus filter (KCF) whose scalability is suitable for a large-scale sensor network with random topology. Based on KCF fusion algorithm, Sigma-Point Information filter (SPIF) is proposed as a micro-filter of KCF to handle the nonlinear dynamic system. Because of its information fusion structure, it is simple and intuitive to be combined with the consensus algorithm. Newly proposed algorithm called Consensus Sigma-Point Information Filter (CSPIF) shows us the improved accuracy compared with local estimates.

关键词： Kalman filtering nonlineaer dynamic systems distributed data fusion

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A Multi-agent Model for Fault Diagnosis in Petrochemical Plants

A Multi-agent Model for Fault Diagnosis in Petrochemical Pla...

引用

IEEE Sensors Applications Symposium (SAS)

作者： Mendoza, Benito Xu, Peng Song, Limin ExxonMobil Res & Engn Co Annandale NJ 08801 USA

ISBN: (纸本)9781424480647

Petrochemical plants are extremely complex systems with many dynamically interconnected components. Traditional approaches to fault detection and diagnosis of these complex systems follow a centralized design in which huge and sophisticated models (e.g., first principle models) are constructed to process sensor data acquired from the entire plant. These systems are very difficult to design due to their complexity. Maintaining such a system to reflect any plant changes (e.g., equipment replacement), is also very challenging. In this article, we introduce a multi-agent model for fault detection and diagnosis which exploits the concept of leadership;that is, when a fault is detected one agent emerges as leader and coordinates the fault classification process. The proposed model is flexible, modular, decentralized, and portable. Our experimental results show that even using simple detection and diagnosis methods, the model can achieve comparable results to those from sophisticated centralized approaches.

关键词： Multi-agent systems modeling distributed data fusion fault diagnosis collective consensus

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Tracking of groups of targets using generalized janossy measure density function

Tracking of groups of targets using generalized janossy meas...

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9th International Conference on Information fusion

作者： Mori, Shozo Chong, Chee-Yee BAE Syst Los Altos CA USA

ISBN: (纸本)9781424409532

This paper describes a new approach to tracking multiple groups of targets using the concept of the generalized Janossy measure density function. Each group is modeled by a group state that consists of a group common state and. an unknown number of individual target component states. In order to represent a probability distribution of such a group state, we propose to use a generalized version of the Janossy measure density function. We will formulate our tracking problem as a distributed estimation problem in which each local sensor processing unit (node) produces single-frame-based group-level detections and a central fusion center (node) fuses those detections into group tracks. A simple illustrative example will be given.

关键词： multiple group target tracking distributed data fusion Janossy measure density function point processes random finite sets

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