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Adaptive data fusion using the expected output membership function

Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 26-33页

作者： Choi, Jongbae Dickerson, Julie A. Iowa State Univ United States

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： sensor data fusion

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Convex optimization approach to the fusion of identity information

Convex optimization approach to the fusion of identity infor...

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Proceedings of the 1999 sensor fusion: architectures, algorithms, and applications iiI

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

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

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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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Neuro-dynamic programming for adaptive fusion complexity control

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 398-409页

作者： Ross, Kenneth N. Chaney, Ronald D. ALPHATECH Inc Burlington United States

The prodigious amount of information provided by surveillance systems and other information sources has created unprecedented opportunities for achieving situation awareness. Because the mission's and user's needs are constantly evolving, fusion control strategies must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called Neuro-Dynamic Programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by using it to allocate computational resources to Bayesian belief networks that use a variety of data types (e.g., SAR, MTI, ELINT, and terrain databases) to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.

关键词： sensor data fusion

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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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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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General software design for multisensor data fusion

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 230-239页

作者： Junliang, Zhang Yuming, Zhao 54th Research Inst of Electronic Industry Shijiazhuang China

In this paper a general method of software design for multisensor data fusion is discussed in detail, which adopts object-oriented technology under UNIX operation system. The software for multisensor data fusion is divided into six functional modules: data collection, database management, GIS, target display and alarming data simulation etc. Furthermore, the primary function, the components and some realization methods of each modular is given. The interfaces among these functional modular relations are discussed. The data exchange among each functional modular is performed by interprocess communication IPC, including message queue, semaphore and shared memory. Thus, each functional modular is executed independently, which reduces the dependence among functional modules and helps software programming and testing. This software for multisensor data fusion is designed as hierarchical structure by the inheritance character of classes. Each functional modular is abstracted and encapsulated through class structure, which avoids software redundancy and enhances readability.

关键词： Computer software

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Neuro-Dynamic Programming for adaptive fusion complexity control

Neuro-Dynamic Programming for adaptive fusion complexity con...

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

作者： Ross, KN Chaney, RD ALPHATECH Inc Burlington MA 01803 USA

ISBN: (纸本)0819431931

The prodigious amount of information provided by surveillance systems and other information sources has created unprecedented opportunities for achieving situation awareness. Because the mission's and user's needs are constantly evolving, fusion control strategics must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called Neuro-Dynamic Programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by using it to allocate computational resources to Bayesian belief networks that use a variety of data types (e.g., SAR, MTI, ELINT, and terrain databases) to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.

关键词： adaptive fusion control Neuro-Dynamic Programming Bayesian networks reinforcement learning

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