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Fuselets - An agent based architecture for fusion of heterogeneous information and data

Fuselets - An agent based architecture for fusion of heterog...

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conference on Multisensor, Multisource Information fusion

作者： Beyerer, Juergen Heizmann, Michael Sander, Jennifer Fraunhofer IITB Inst Informat & Datenverarbeitung Fraunhoferstr 1 D-76131 Karlsruhe Germany

ISBN: (纸本)0819462985

A new architecture for fusing information and data from heterogeneous sources is proposed. The approach takes criminalistics as a model. In analogy to the work of detectives, who investigate crimes, software agents are instantiated that pursue clues and try to consolidate or to dismiss hypotheses. Like their human pendants, they can, if questions beyond their competencies arise, consult expert agents. Within the context of a certain task, region, and time interval, specialized operations are applied to each relevant information source, e. g. IMINT, SIGINT, ACINT,..., HUMINT*, databases etc. in order to establish hit lists of first clues. Each clue is described by its pertinent facts, uncertainties, and dependencies in form of a local degree-of-belief (DoB) distribution dagger in a Bayesian sense. For each clue, an agent is instantiated which cooperates with other agents and experts. Expert agents support the use of different information sources. Consultation of experts capable of accessing certain information sources results in changes of the DoB of the pertinent clue. According to the significance of concentration of their DoB distribution, clues are abandoned or pursued further to formulate task specific hypotheses. Communication between the agents serves to find out whether different clues belong to the same cause and thus can be combined. At the end of the investigation process, the different hypotheses are evaluated by a jury and a final report is created that constitutes the fusion result. The approach proposed avoids calculating global DoB distributions by adopting a local Bayesian approximation and thus reduces the complexity of the problem essentially. Different information sources are transformed into DoB distributions using the maximum entropy paradigm and considering known facts as constraints. Nominal, ordinal, and cardinal quantities can be treated within this framework equally. The architecture is scalable by tailoring the number of agents according t

关键词： heterogeneous information sources Bayesian fusion agent technology criminalistics local Bayesian approach artificial intelligence man machine interaction representation of uncertainty

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Target Tracking in a Two-Tiered Hierarchical sensor Network

Target Tracking in a Two-Tiered Hierarchical Sensor Network

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M. Vemula M.F. Bugallo P.M. Djuric Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

An important application of sensor networks is target tracking and localization. To deal with sensor nodes with limited energy supply and communication bandwidth we propose energy-efficient hierarchical architectures for solving the target tracking problem. In these networks, sensors form clusters and transmit minimal quantized information about a sensed event to a specialized node, known as a cluster head. Cluster heads are equipped with capability of communicating over large distances with a fusion center or a base station. We consider two different hierarchical architectures: (a) the target dynamics are probabilistically estimated at the cluster heads and their statistics combined at the fusion center, and (b) the cluster heads perform simple compression rules on the quantized sensor data and the fusion center estimates the target dynamics using these severely compressed data. Sequential Monte Carlo algorithms for estimation of the target dynamics are used. Through computer simulations the performances of these two architectures are studied

关键词： Target tracking Bandwidth Energy efficiency Base stations Statistics sensor fusion Monte Carlo methods Clustering algorithms Computer simulation Computer architecture

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Real-time fusion of multimodal tracking data and generalization of motion patterns for trajectory prediction

Real-time fusion of multimodal tracking data and generalizat...

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IEEE International conference on Information Acquisition

作者： Weser, Martin Westhoff, Daniel Hueser, Markus Zhang, Jianwei Univ Hamburg Dept Informat Inst Tech Aspects Multimodal Syst Martinistr 52 Hamburg Germany

ISBN: (纸本)1424405289

A sensor-based model of a service robot's environment is a prerequisite for interaction. Such a model should contain the positions of the robot's interaction partners. Many reasonable applications require this knowledge in real-time. It could for example be used to realize efficient path planning for delivery tasks. Additionally to the actual positions of the partners it is important for the service robot to predict their possible future positions. In this paper we propose an extensible framework that combines different sensor modalities in a general real-time tracking system. Exemplarily, a tracking system is implemented that fuses tracking algorithms in laser range scans as well as in camera images by a particle filter. Furthermore, human trajectories are predicted by deducing them from learned motion patterns. The observed trajectories are generalized to trajectory patterns by a novel method which uses Self Organizing Maps. Those patterns are used to predict trajectories of the currently observed persons. Practical experiments show that multimodatity increases the system's robustness to incorrect measurements or single sensors. It is also demonstrated that a Self Organizing Map is suitable for learning and generalizing trajectories. Convenient predictions of future trajectories are presented which Eire deduced from these generalizations.

关键词： Self organizing maps

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Fast and stable tracking for AR fusing video and inertial sensor data

Fast and stable tracking for AR fusing video and inertial se...

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14th International conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2006, WSCG'2006 - In Co-operation with EUROGRAPHICS

作者： Bleser, Gabriele Wohlleber, Cedric Becker, Mario Stricker, Didier Fraunhofer IGD Fraunhoferstraße 5 64283 Darmstadt Germany

ISBN: (纸本)8086943038

Accurate acquisition of camera position and orientation is crucial for realistic augmentations of camera images. Computer vision based tracking algorithms, using the camera itself as sensor, are known to be very accurate but also time-consuming. The integration of inertial sensor data provides a camera pose update at 100 Hz and therefore stability and robustness against rapid motion and occlusion. Using inertial measurements we obtain a precise real time augmentation with reduced camera sample rate, which makes it usable for mobile AR and See-Through applications. This paper presents a flexible run-time system, that benefits from sensor fusion using Kalman filtering for pose estimation. The camera as main sensor is aided by an inertial measurement unit (IMU). The system presented here provides an autonomous initialisation as well as a predictive tracking procedure and switches between both after successfull (re)-initialisation and tracking failure respectively. The computer vision part performs 3D model-based tracking of natural features using different approaches for yielding both, high accuracy and robustness. Results on real and synthetic sequences show how inertial measurements improve the tracking. Copyright © UNION Agency - Science Press.

关键词： Augmented reality

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Multi-sensor Coordination And fusion For Automotive Safety applications

Multi-Sensor Coordination And Fusion For Automotive Safety A...

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International conference on Information fusion

作者： N. Floudas A. Polychronopoulos M. Tsogas A. Amditis Institute of Communications and Computer Systems Athens Greece

ISBN: (纸本)1424409535

This paper focuses on the solution of the problem of (onboard moving vehicles) multiple sensor data fusion systems. The proposed application uses distributed architectures that operate with sensors or sensor systems and give redundant or complementary information for moving objects. This architecture ensures a modular approach allowing exchangeability and benchmarking using the output of individual trackers, whereas the fusion algorithm gives a solution to the track management problem and the coverage of wide perception areas. The test case is a multi-sensor configuration, which monitors the rear and lateral areas of traffic. Results from simulations and real data show that the given approach allows maintenance of the ID of objects and recognition of the vehicle environment with acceptable rates of false alarm and misses

关键词： Automotive engineering Safety sensor fusion Radar tracking sensor systems and applications sensor systems Laser radar Radar applications Traffic control Vehicles

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Data fusion in sensor Networks

Data Fusion in Sensor Networks

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IEEE conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Hugh Durrant-Whyte ARC Centre of Excellence for Autonomous Systems University of Sydney Australia

There is a growing excitement about the potential application of large scale sensor networks in diverse applications such as precision agriculture, geophysical and environment monitoring, remote health care, and security. Rapid progress in sensing hardware, communications and low-power computing has resulted in a profusion of commercially available sensor nodes. The big challenge now is to develop effective methods for the automatic fusion and interpretation of the information generated by large-scale sensor networks. The success of future applications is predicated on finding solutions to this data fusion challenge. This talk will focus on probabilistic models and Bayesian data fusion methods appropriate to describing and solving sensor network data fusion problems. Over a number of years, we have used such methods to develop decentralised algorithms for point-target estimation in sensor networks. More recently, other researchers have developed distributed Bayesian algorithms for sensor networks to estimate field properties, such as temperature or humidity over an area. The prospect of developing holistic Bayesian methods for fusion in sensor networks now looks to be a real and exciting possibility. This talk will describe these potential developments. Notions of divergence and information appear naturally in probabilistic fusion algorithms. In turn these provide a handle on two other sensor network data fusion problems; how to model and manage sensor node performance and how to learn patterns in or interpret sensor network information. These ideas will also be developed in this talk. Interspersed amongst these developments we will describe some real sensor network applications, some solved, some currently being addressed, and some that remain as a challenge to network data fusion methods.

关键词： sensor fusion Bayesian methods Large-scale systems Geophysics computing sensor phenomena and characterization Temperature sensors Agriculture Remote monitoring Medical services Data security

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Towards scalable and robust distributed intrusion alert fusion with good load balancing

Towards scalable and robust distributed intrusion alert fusi...

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ACM SIGCOMM 2006 - conference on applications, Technologies, architectures, and Protocols for Computer Communication

作者： Li, Zhichun Chen, Yan Beach, Aaron Department of Electrical Engineering and Computer Science Northwestern University 2145 Sheridan Road Evanston IL 60208 United States

ISBN: (纸本)1595935711

Traffic anomalies and distributed attacks are commonplace in today's networks. Single point detection is often insufficient to determine the causes, patterns and prevalence of such events. Most existing distributed intrusion detection systems (DIDS) rely on centralized fusion, or distributed fusion with unscalable communication mechanisms. In this paper, we propose to build a DIDS based on the emerging decentralized location and routing infrastructure: distributed hash table (DHT). We embed the intrusion symptoms into the DHT dimensions so that alarms related to the same intrusion (thus with similar symptoms) will be routed to the same sensor fusion center (SFC) while evenly distributing unrelated alarms to different SFCs. This is achieved through careful routing key design based on: 1) analysis of essential characteristics of four common types of intrusions: DoS attacks, port scanning, virus/worm infection and botnets;and 2) distribution and stability analysis of the popular port numbers and those of the popular source IP subnets in scans. We further propose several schemes to distribute the alarms more evenly across the SFCs, and improve the resiliency against the failures or attacks. Evaluation based on one month of DShield firewall logs (600 million scan records) collected from over 2200 worldwide providers show that the resulting system, termed Cyber Disease DHT (CDDHT), can effectively fuse related alarms while distributing unrelated ones evenly among the SFCs. It significantly outperforms the traditional hierarchical approach when facing large amounts of diverse intrusion alerts. Copyright 2006 ACM.

关键词： Intrusion detection

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Multi level processing methodology for automotive applications

Multi level processing methodology for automotive applicatio...

引用

International conference on Intelligent Transportation

作者： U. Scheunert P. Lindner H. Cramer T. Tatschke A. Polychronopoulos G. Wanielik Chemnitz University of Technology Chemnitz Germany Institute for Software Systems Technical Applications of Computer Science University of Passau Passau Germany Institute of Communication and Computer Systems Athens Greece

The fusion of data from different sensorial sources is nowadays an often-used method to increase robustness and reliability of automatic environmental perception. The project Profusion2, which is a horizontal subproject in the IP PReVENT (funded by the EC) was created to enhance fusion techniques and algorithms beyond the current state-of-the-art. The enhancement of the algorithms is strongly connected with the creation of a methodology to describe vehicle environments in an adequate manner to meet the requirements of robustness and reliability. In this paper, the definition of such a general environmental description is proposed and an according general data structure is introduced. This data structure is able to handle all kind of information occurring in a data fusion process. Additionally, an output structure of the perception system is proposed to work as an interface to the applications. The Profusion2 community suggested a model for sensor data fusion in compliance with the Joint Directors of Laboratories (JDL) model which is a widely known model for information fusion systems (Hall and Llinas, 2001)

关键词： Automotive applications Vehicle safety sensor fusion Data structures Road safety sensor systems and applications Robustness sensor systems Environmental management Intelligent sensors

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Scalability of Location sensor Data fusion

Scalability of Location Sensor Data Fusion

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conference on Local Computer Networks (LCN)

作者： Tom Pfeifer Kieran Sullivan Micheal O Foghlu Telecommunications Software & Systems Group Waterford Institute of Technology Waterford Ireland

Positioning, as one of the prime components of context, has been a driving factor in the development of ubiquitous computing applications throughout the past two decades. Based on the redundant positioning architecture, this paper discusses the issues of suitability and scalability of algorithms for sensor data fusion from analytical and practical perspectives.

关键词： Scalability sensor fusion Computer architecture sensor systems sensor systems and applications sensor phenomena and characterization Algorithm design and analysis Concurrent computing Bayesian methods Software systems

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Data fusion in multi sensor platforms for wide-area perception

Data fusion in multi sensor platforms for wide-area percepti...

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IEEE Symposium on Intelligent Vehicle

作者： A. Polychronopoulos N. Floudas A. Amditis D. Bank B. van den Broek Institute of Communications and Computer Systems Athens Greece Daimler Chrysler Aerospace Ulm Germany TNO Defensie en Veiligheid Den Haag Netherlands

There is a strong belief that the improvement of preventive safety applications and the extension of their operative range are achieved by the deployment of multiple sensors with wide fields of view (FOV). The paper contributes to the solution of the problem and introduces distributed sensor data fusion architectures and algorithms for an efficient deployment of multiple sensors that give redundant or complementary information for the moving objects. The proposed fusion architecture is based on a modular approach allowing exchangeability and benchmarking using the output of individual trackers, whereas the fusion algorithm gives a solution to the track management problem and the coverage of wide perception areas. The test case is LATERAL SAFE sensor configuration, which monitors the rear and lateral areas of the vehicle. Results show that with the given approach the system is able to maintain the ID of all objects in transition (an object enters a sensor's FOV) and blind areas (no sensor coverage)

关键词： sensor fusion sensor systems Laser radar Infrared sensors Vehicle safety Accidents sensor systems and applications Radar tracking Intelligent sensors Benchmark testing

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