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Convex optimization approach to the fusion of identity information

Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 389-397页

作者： Li, Lingjie Luo, Zhi-Quan Wong, K.Max Bosse, Eloi McMaster Univ Hamilton Canada

We consider the problem of identify fusion for a multi-sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identify fusion approach based on the minimization of inconsistencies between the sensor reports by using a convex Quadratic Programming (QP) and linear programming (LP) formulation. In contrast to the Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach is capable of fusing 'Ratio type' sensor reports, thus it is more general than the evidential reasoning theory. When the sensor reports are consistent, the solution generated by the new fusion method can be shown to converge to the true probability distribution. Simulation work shows that our method generates reasonable fusion results, and when only 'Subset type' sensor reports are present, it produces fusion results similar to that obtained via the evidential reasoning theory.

关键词： sensor data fusion

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Feature level sensor fusion

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 332-338页

作者： Peli, Tamar Young, Mon Knox, Robert Ellis, Ken Bennett, Fredrick Atlantic Aerospace Electronics Corp Waltham United States

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： sensor data fusion

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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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Multitarget tracking algorithm performance evaluation

Multitarget tracking algorithm performance evaluation

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

作者： Zheng, H Farooq, M Main, R Royal Mil Coll Canada Dept Elect & Comp Engn Kingston ON K7K 5L0 Canada

ISBN: (纸本)0819431931

This paper discusses some problems in evaluating the performance of multi-target tracking (MTT) systems. various performance measures for the MTT systems are first described. These include: correlation statistics;track purity;track maintenance statistics;and kinematic statistics. Examples of single measures of performance are also given. The issues involved in the analytical prediction of performance are briefly discussed. Detailed descriptions of the computer simulation evaluation for the MTT systems include: test scenario selection, sensor modeling, data collection and the analysis of results. Two performance evaluation methods, namely: a two step method and a track classification approach are explored in this paper. The performance evaluation techniques are being Incorporated in a MTT test bed developed in the Department of Electrical and Computer Engineering at the Royal Military College of Canada, Kingston, Ontario, Canada.

关键词： multi-sensor multi-target tracking performance evaluation techniques

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Adaptive data fusion using the expected output membership function

Adaptive data fusion using the expected output membership fu...

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

作者： Choi, JB Dickerson, JA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)0819431931

Fuzzy set methods can improve the fusion of uncertain sensor data. The expected output membership function (EOMF) method uses the fuzzified inputs and possible fuzzy outputs to estimate the fused output. The most likely fuzzy output comes from fusability measures which are calculated using the degrees of the intersections of the possible fuzzy outputs with the fuzzified inputs. The support lengths of the fuzzified inputs can be set proportional to the sensor variance in the fixed case. However, individual measurements can deviate widely from the true value even in accurate sensors. The support length of input sets can be varied by estimating the variation of the input. This adaptation helps deal with occasional bad or noisy measurements. The variation is defined as the absolute change rate of the input with respect to previous output estimates. The EOMF can also be too wide or too narrow compared to the fuzzified inputs. Adaptive methods can help select the size of the EOMF. An example from the control of automated vehicles shows the effectiveness of the adaptive EOMF method, compared to the fixed EOMF method and the weighted average method. The EOMF method shows robustness to outlying measurements when the average fusion operator is used.

关键词： data fusion fuzzy sets fuzzy systems neuro-fuzzy control

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sensor fusion with application to Electronic Warfare

Sensor fusion with application to Electronic Warfare

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

作者： Zanzalari, RM Van Alstine, E Night Vis & Elect Sensors Directorate AMSEL RD NVS CCD Ft Monmouth NJ 07703 USA

ISBN: (纸本)0819431931

The Night Vision and Electronic sensors Directorate, Survivability/Camouflage, Concealment and Deception Division mission is to provide affordable aircraft and ground electronic sensors/systems and signature management technologies which enhance survivability and lethality of U.S. and International Forces. Since 1992, efforts have been under tah-en in the area of Situational Awareness and Dominant Battlespace Knowledge. These include the Radar Deception and Jamming Advanced Technology Demonstration (ATD), Survivability and Targeting System Integration, Integrated Situation Awareness and Targeting ATD, Combat Identification, Ground Vehicle Situational Awareness, and Combined Electronic Intelligence (ELINT) Target Correlation. This paper will address the Situational Awareness process as it relates to the integration of Electronic Warfare (EW) with targeting and intelligence and information warfare systems. Discussion will be presented on the sensor fusion, Situation Assessment and Response Management Strategies. sensor fusion includes the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats as well as their significance. Situation Assessment includes the process of interpreting and expressing the environment based on situation abstract products and information fi om technical and doctrinal data bases. Finally, Response Management provides the centralized, adaptable control of all renewable and expendable countermeasure assets resulting in optimization of the response to the threat environment.

关键词： Electronic Warfare sensors sensor data fusion Situational Awareness situation assessment Response Management association and correlation

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

Fusion of imaging and non-imaging sensor information for air...

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

作者： Valin, P Jouan, A Bossé, E Lockheed Martin Canada Montreal PQ H4P 1K6 Canada

ISBN: (纸本)0819431931

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： imaging non-imaging Dempster-Shafer testbed evidential reasoning maritime surveillance

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Revisions to the JDL data fusion model

Revisions to the JDL data fusion model

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

作者： Steinberg, AN Bowman, CL White, FE ERIM Int Inc Arlington VA 22209 USA

ISBN: (纸本)0819431931

The Data fusion Model maintained by the Joint Directors of Laboratories (JDL) Data fusion Group is the most widely-used method for categorizing data fusion-related functions. This paper discusses the current effort to revise and expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems. Data fusion involves combining information - in the broadest sense - to estimate or predict the state of some aspect of the universe. These may be represented in terms of attributive and relational states. If the job is to estimate the state of a people (or any other sentient beings), it can be useful to include consideration of informational and perceptual stares in addition to the physical state. Developing cost-effective multi-source information systems requires a method for specifying data fusion processing and control functions, interfaces, and associated databases. The lack of common engineering standards for data fusion systems has been a major impediment to integration and re-use of available technology: current developments do nor lend themselves to objective evaluation, comparison or re-use. This paper reports on proposed revisions and expansions of the JDL Data fusion model to remedy some of these deficiencies. This involves broadening the functional model and related taxonomy beyond the original military focus, and integrating the Data fusion Tree Architecture model for system description, design and development.

关键词： Data fusion Data modeling Systems modeling Standards development Data processing sensors Algorithm development Data analysis Systems engineering Signal detection

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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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