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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Slamani, MA Varshney, PK Alford, MG Ferris, D Stiefvater Consultants Inc Marcy NY 13403 USA

ISBN: (纸本)0819431931

A number of sensors are being developed for the Concealed Weapon Detection (CWD), and use of the appropriate sensor or combination of sensors will be: very important to the success of such technologies. Assuming that two identical sensors are used to collect data on a target from different angular views, this paper addresses the problem of registration associated with the collected scenes. Theory and application to real data are presented.

关键词： image registration Concealed Weapons Detection A'SCAPE enhancement fusion

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sensor fusion cognition using belief filtering for tracking and identification

Sensor fusion cognition using belief filtering for tracking ...

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Blasch, E Hong, L SNAT AFRL Wright Patterson AFB OH 45433 USA

ISBN: (纸本)0819431931

Humans exhibit remarkable abilities to estimate, filter, predict, and fuse information in target tracking tasks. To improve track quality, we extend previous tracking approaches by investigating human cognitive-level fusion for constraining the set of plausible targets where the number of targets is not known a priori. The target track algorithm predicts a belief in the position and pose for a set of targets and an automatic target recognition algorithm uses the pose estimate to calculate an accumulated target-belief classification confidence measure. The human integrates the target track information and classification confidence measures to determine the number and identification of targets. This paper implements the cognitive belief filtering approach for sensor fusion and resolves target identify through a set-theory approach by determining a plausible set of targets being tracked.

关键词： sensor fusion target recognition classification & identification tracking cognitive belief filter HRR

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Fuzzy sensor fusion for gas turbine power plants

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 52-61页

作者： Goebel, Kai Agogino, Alice GE Corp Research & Development Niskayuna United States

In this paper we present a methodology for fuzzy sensor fusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) It aggregates redundant (but uncertain) sensor information; this allows making decisions which sensors (and to what degree) should be considered for propagation of sensor information. 2.) It filters out noise and sensor failure from measurements; this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading from validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.

关键词： sensor data fusion

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Information fusion benefits delineation in off-nominal scenarios

Information fusion benefits delineation in off-nominal scena...

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Dasarathy, BV Dynet Inc Huntsville AL 35814 USA

ISBN: (纸本)0819431931

The potential problem of deterioration In recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the results of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in - decision out (FEI-DEO) fusion as well as decisions in - decision out (DEI-DEO) fusion. The latter spans across both information sources (sensors) and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in various levels of acoustical noise.

关键词： information fusion off-nominal scenarios audio-visual fusion vowel recognition

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Cross band pixel selection in multi-resolution image fusion

Cross band pixel selection in multi-resolution image fusion

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Petrovic, V Xydeas, C Univ Manchester Manchester Sch Engn MARC Manchester M13 9PL Lancs England

ISBN: (纸本)0819431931

The work described in this paper focuses on cross band pixel select:ion as applied to pixel level multi-resolution image fusion. In addition, multi-resolution analysis and synthesis is realised via. QMF sub-band decomposition techniques. Thus cross-band pixel selection is-considered with the aim of reducing the contrast and structural distortion image artefacts produced by existing wavelet based, pixel level, image fusion schemes. Preliminary subjective image fusion results demonstrate clearly the advantage which the proposed cross-band selection technique offers, when compared to conventional area based pixel selection.

关键词： image fusion multi-resolution image processing sensor fusion

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Development of emergent processing loops as a system of systems concept

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 186-195页

作者： Gainey, James Blasch, Erik US Air Force Research Lab Wright-Patterson AFB United States

A System for Systems (SoS) design is introduced for improving the overall performance, capabilities, operational robustness, and user confidence in Identification (ID) systems. The physio-associative temporal sensor integration algorithm (PATSIA) is used. The SoS architecture proposes dynamic sensor and knowledge-source integration by implementing multiple Emergent Processing Loops (EPL) for Predicting, feature Extracting, Matching, and Searching both static and dynamic databases. These objectives are demonstrated by modeling similar processes from the eyes, ears, and somatosensory channels, through the thalamus, and to cortices as appropriate while using the hippocampus for short-term memory search and storage as necessary.

关键词： sensor data fusion

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Fuzzy sensor fusion for gas turbine power plants

Fuzzy sensor fusion for gas turbine power plants

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Goebel, K Agogino, A GE Corp Res & Dev Niskayuna NY 12309 USA

ISBN: (纸本)0819431931

In this paper we present a methodology for fuzzy sensor fusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) it aggregates redundant (but uncertain) sensor information;this allows making decisions which sensors land to what degree) should be considered for propagation of sensor information. 2.) It filters out noise and sensor failure from measurements;this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading from validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.

关键词： fuzzy fusion fuzzy validation fuzzy monitoring diagnosis

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Feature level sensor fusion

Feature level sensor fusion

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Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Peli, T Young, M Knox, R Ellis, K Bennett, F Atlantic Aerosp Elect Corp Waltham MA 02154 USA

ISBN: (纸本)0819431931

This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor, However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial False alarm reduction. The approaches are easily extendable to more than two sensors.

关键词： sensors Image fusion Data fusion Synthetic aperture radar Target detection Feature extraction sensor fusion Image processing Infrared sensors Target recognition

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sensor fusion options for ballistic missile defense interceptor applications

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 92-102页

作者： Bjork, Charles Morris, Norman Dasarathy, Belur V. Smith, Bart Allen, Doug Prestwood, William T. Nichols Research Corp Huntsville United States

Critical elements of future exoatmospheric interceptor systems are intelligent processing (IP) techniques which can effectively combine sensor data from disparate sensors. This paper summarizes the impact on discrimination performance of several feature and classifier fusion techniques, which can be used as part of the overall IP approach. These techniques are implemented either within the Fused sensor Discrimination (FuSeD) Testbed, or off-line as building blocks that can be modified to assess differing fusion approaches, classifiers and their impact on interceptor requirements. Several optional approaches for combining the data at the different levels, i.e, feature and classifier levels, are discussed in this paper and a comparison of performance results is shown. Approaches yielding promising results must still operate within the timeline and memory constraints on board the interceptor. A hybrid fusion approach is implemented at the feature level through the use of feature sets input to specific classifiers (currently two classifiers are employed). The output of the fusion process contains an estimate of the confidence in the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data, classifiers, or to request additional sensor data on specific objects that have not been confidently identified as 'lethal' or 'non-lethal'. However, dynamic selection requires an understanding of the impact of various combinations of feature sets and classifier options. Accordingly, the paper presents the various tools for exploring these options and illustrates their usage with data sets generated to realistically simulate the world of Ballistic Missile Defense (BMD) interceptor applications.

关键词： sensor data fusion

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fusion of imaging and non-imaging sensor information for airborne surveillance

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 126-138页

作者： Valin, Pierre Jouan, Alexandre Bosse, Eloi Lockheed Martin Canada Montreal Canada

This paper presents results from an Adaptable Data fusion Testbed (ADFT) which has been constructed to analyze simulated or real data with the help of modular algorithms for each of the main fusion functions and image interpretation algorithms. The results obtained from data fusion of information coming from an imaging Synthetic Aperture Radar (SAR) and non-imaging sensors (ESM, IFF, 2-D radar) on-board an airborne maritime surveillance platform are presented for two typical scenarios of Maritime Air Area Operations and Direct Fleet Support. An extensive set of realistic databases has been created that contains over 140 platforms, carrying over 170 emitters and representing targets from 24 countries. A truncated Dempster-Shafer evidential reasoning scheme is used that proves robust under countermeasures and deals efficiently with uncertain, incomplete or poor quality information. The evidential reasoning scheme can yield both single ID with an associated confidence level and more generic propositions of interest to the Commanding Officer. For nearly electromagnetically silent platforms, the Spot Adaptive mode of the SAR, which is appropriate for naval targets, is shown to be invaluable in providing long range features that are treated by a 4-step classifier to yield ship category, type and class. Our approach of reasoning over attributes provided by the imagery will allow the ADFT to process in the next phase (currently under way) both FLIR imagery and SAR imagery in different modes (RDP for naval targets, Strip Map and Spotlight Non-Adaptive for land targets).

关键词： sensor data fusion

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