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conference on sensor fusion - architectures, algorithms, and applications V

作者： Goebel, K Mysore, SP GE Corp Res & Dev Niskayuna NY 12309 USA

ISBN: (纸本)0819440809

This paper presents methods to boost the classification rate in decision fusion with partially redundant information. This is accomplished by utilizing the information of known misclassifications of certain classes to systematically modify class output. For example, if it is known beforehand that tool A misclassifies class 1 often as class 2, then it appears to be prudent to integrate that information into the reasoning process if class 1 is indicated by tool B and class 2 is observed by tool A. Particularly this preferred misclassification information is contained in the asymmetric (cross-correlation) entries of the confusion matrix. An operation we call "cross-correlation" is performed where this information is explicitly used to modify class output before the first fused estimate is calculated. We investigate several methods for cross-correlation and discuss the advantages and disadvantages of each. We then apply the concepts introduced to the diagnostic realm where we aggregate the output of several different diagnostic tools. We show how the proposed approach fits into an information fusion architecture and finally present results motivated from diagnosing on-board faults in aircraft engines.

关键词： classification diagnostics information fusion decision fusion correlation cross-correlation

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Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

Colour-the-INSight: combining a direct view rifle sight with...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Hogervorst, M. A. Jansen, C. Toet, A. Bijl, P. Bakker, P. Hiddema, A. C. van Vliet, S. F. TNO Human Factors POB 23 NL-3769 GZ Soesterberg Netherlands Thales Netherland BV Huizen Netherlands Photonis Netherlands BV NL-9300 AB Groningen Netherlands Minist Def Joint Soldier & Equipment Ctr Expertise The Hague Netherlands

ISBN: (纸本)9780819490858

We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e. g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined

关键词： image fusion false colour natural colour mapping real-time fusion night vision

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Adaptive IR and VIS image fusion

Adaptive IR and VIS image fusion

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Stoklasa, B. Rehacek, J. Hradil, Z. Palacky Univ Dept Opt Olomouc 77146 Czech Republic

ISBN: (纸本)9780819490858

Adaptive image fusion system based on neural network principle was realized. It works with digitalized video sequences of visible and infrared band sensors, and is able to produce the optimal fused image for a wide range of lighting conditions through an adaptive change of a fusion algorithm. The change is driven by a change in the measured statistic of the input images. The best algorithm for a particular input is found with the help of an objective measurement of the fusion process quality.

关键词： Image fusion Adaptive fusion Neural network

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Data fusion architectures for sensor platforms

Data fusion architectures for sensor platforms

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2008 IEEE Aerospace conference

作者： Mirza, Atif R. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781424414871

The role of data fusion in sensor platforms is becoming increasingly important in various domains of science, technology and business. fusion pertains to the merging or integration of information towards an enhanced level of awareness. Multi-sensor fusion can be applied to any system that must retrieve and synthesize data from numerous sources. Its applications can be found in a variety of technology settings, from robotics, structural and vehicle health monitoring to communications, space science and telemedicine. This work is a canonical overview of several major fusion architectures developed from the remote sensing and defense community. We present several types of architecture for managing multi-sensor data fusion, specifically as they relate to the tracking-correlation function and blackboard processing representations in knowledge engineering. Object-Process Methods are used to model the information fusion process and supporting subsystems. Finally, we discuss the importance of fusion to the concept and operation of the Semantic Web. Semantic networks offer powerful ways to exploit the synergy of multi-sensor data platforms. Their operation is based on synthesis of fusion with ontology models for knowledge representation. We discuss the importance of fusion as a reuse process in ontological engineering, and review key lifecycle models in ontology development. The evolutionary approach to ontology development is considered the most useful and adaptable to the complexities of semantic networks. Several potential applications for data fusion are screened and ranked according to the Joint Directors of Laboratories (JDL) process model for information fusion. Based on these predetermined criteria, the case of medical diagnostic imaging was found to offer the most promising applications for fusion, on which future product platforms can be built.(1).

关键词： Structural health monitoring

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Distributed multi-sensor fusion

Distributed multi-sensor fusion

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2008

作者： Scheffel, Peter Fish, Robert Knobler, Ron Plummer, Thomas McQ Inc. 1551 Forbes Street Fredericksburg VA 22405-1603

ISBN: (纸本)9780819471659

McQ has developed a broad based capability to fuse information in a geographic area from multiple sensors to build a better understanding of the situation. The paper will discuss the fusion architecture implemented by McQ to use many sensors and share their information. This multi sensor fusion architecture includes data sharing and analysis at the individual sensor, at communications nodes that connect many sensors together, at the system server/user interface, and across multi source information available through networked services. McQ will present a data fusion architecture that integrates a "Feature Information Base" (FIB) with McQ's well known Common Data Interchange Format (CDIF) data structure. The distributed multi sensor fusion provides enhanced situation awareness for the user.

关键词： Information fusion

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Pattern-level temporal difference learning, data fusion, and chess

Pattern-level temporal difference learning, data fusion, and...

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conference on sensor fusion: architectures, algorithms, and applications IV

作者： Levinson, R Weber, R Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)0819436771

Our research group is using chess as a vehicle for studying the fusion of adaptation, multiple representation, and search technologies for real-time decision making. Chess systems like Deep Blue have achieved Grandmaster chess play with a brute-force search of the game tree and human-supplied information, like piece-values and opening books. However, subtle aspects of chess, including positional features and advanced concepts, are not capable of being represented or processed efficiently with the standard method. Since 1989, Morph I-III have exhibited more autonomy and learning ability than traditional chess programs in "adaptive pattern-oriented chess". (2) Like its predecessors, Morph TV is a reinforcement learner, but it also uses a new technique we call pattern-level TD and Q-learning to mathematically map the state space and effectively learn to classify situations. Its three knowledge sources include two traditional ones: material and a piece-square table, and a new method called Distance. These are combined using a simple genetic algorithm and a decision tree. This paper shows the effectiveness of fusing knowledge to replace search in real-time situations, since an agent which combines all sources is capable of consistently beating an agent which employs any of the individual knowledge sources. Surprisingly, the pattern-level TD agent is slightly superior to the pattern-level Q-learning agent, despite the fact that the Q-learning agent updates more Q-values on each temporal step.

关键词： Q-learning temporal difference decision trees genetic algorithms chess search distance pattern oriented knowledge acquisition

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Image processing, radiological, and clinical information fusion in breast cancer detection

Image processing, radiological, and clinical information fus...

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conference on sensor fusion - architectures, algorithms, and applications VI

作者： Alto, H Rangayyan, RM Solaiman, B Desautels, JEL MacGregor, JH Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

ISBN: (纸本)0819444812

Screening mammography is the most efficient and cost-effective method available for detecting the signs of early breast cancer in asymptomatic women between the ages of 50 and 69. To improve the detection rate and reduce the number of unnecessary biopsies, many different computer-aided diagnosis techniques have been developed(15,26). Many, of these techniques use image processing algorithms to automatically segment and classify the images. The decision-making process associated with the evaluation of mammograms is complex and incorporates multiple sources of information from standard medical knowledge and radiology to pathology. The use of this information combined with the results of image processing offers new challenges to the field of data and information fusion. In this paper, we describe the different information sources and their data as well as the framework that is needed to support this type of fusion. A database of breast cancer screening cases forms the basis of the resulting fusion model. The database and decision-level fusion techniques will facilitate unique and specialized approaches for efficient and sophisticated diagnosis of breast cancer.

关键词： Breast cancer Image processing Databases Mammography Information fusion Breast Data fusion Cancer Image fusion Image segmentation

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Improved decentralized multi-sensor navigation system for airborne applications

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GPS SOLUTIONS 2018年第3期22卷 1-13页

作者： Jiang, Wei Li, Yong Rizos, Chris Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran State Key Lab Rail Traff Control & Safety Beijing Peoples R China Navexplorer Technol Sydney NSW Australia Univ New South Wales Sch Civil & Environm Engn Sydney NSW Australia

The integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) technologies is a very useful navigation option for high-accuracy positioning in many applications. However, its performance is still limited by GNSS satellite availability and satellite geometry. To address such limitations, a non-GNSS-based positioning technology known as "Locata" is used to augment a standard GNSS/INS system. The conventional methods for multi-sensor integration can be classified as being either in the form of centralized Kalman filtering (CKF), or decentralized Kalman filtering. However, these two filtering architectures are not always ideal for real-world applications. To satisfy both accuracy and reliability requirements, these three integration algorithms-CKF, federated Kalman filtering (FKF) and an improved decentralized filtering, known as global optimal filtering (GOF)-are investigated. In principle, the GOF is derived from more information resources than the CKF and FKF algorithms. These three algorithms are implemented in a GPS/Locata/INS integrated navigation system and evaluated using data obtained from a flight test. The experimental results show that the position, velocity and attitude solution derived from the GOF-based system indicate improvements of 30, 18.4 and 20.8% over the CKF- and FKF-based systems, respectively.

关键词： GNSS Locata INS Multi-sensor navigation Data fusion

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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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31st IEEE International conference on Acoustics, Speech and Signal Processing

作者： Vemula, Mahesh Bugallo, Monica F. Djuric, Petar M. SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

ISBN: (纸本)9781424404681

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： sensor networks

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Assessment of data and knowledge fusion strategies for prognostics and health management

Assessment of data and knowledge fusion strategies for progn...

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IEEE Aerospace conference

作者： Roemer, MJ Kacprzynski, GJ Orsagh, RF Impact Technol LLC Rochester NY 14623 USA

ISBN: (纸本)0780365992

Various data, feature and knowledge fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of anomaly, diagnostic and prognostic technologies. fusion of relevant sensor data, maintenance database information, and outputs from various diagnostic and prognostic technologies has proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. The data fusion strategies discussed in this paper are principally probabilistic in nature and are used to aid in directly identifying confidence bounds associated with specific component fault identifications and predictions. Dempster-Shafer fusion, Bayesian inference, fuzzy-logic inference, neural network fusion and simple weighting/voting are the algorithmic approaches that are discussed in this paper. Data fusion architectures such as centralized fusion, autonomous fusion, and hybrid fusion are described in terms of their applicability to fault diagnosis and prognosis. The final goal is to find the optimal combination of measured system data. data fusion algorithms, and associated architectures for obtaining the highest overall prediction/detection confidence levels associated with a specific application. To achieve this goal, a set of metrics has been developed for gauging the performance and effectiveness of a fusion strategy. Specifically, this paper will demonstrate how various metrics are used for assessing individual and fused vibration-based diagnostic algorithms. Evaluation of the diagnostic strategies was performed using gearbox seeded-fault and accelerated failure data taken with the MDTB (Mechanical Diagnostic Test Bed) at the ARL Lab at Penn State University.

关键词： sensor data fusion

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