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

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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Multi-spectral image fusion for visual display

Multi-spectral image fusion for visual display

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

作者： Peli, T Peli, E Ellis, K Stahl, R Atlantic Aerosp Elect Corp Waltham MA 02154 USA

ISBN: (纸本)0819431931

This paper describes a contrast-based monochromatic fusion process. The fusion process is aimed for on board real time application and it is based on practical and computationally efficient image processing components. The process maximizes the information content in the combined image, while retaining visual clues that are essential for navigation/piloting tasks. The method is a multi scale fusion process that provides a combination of pixel selection from a single image and a weighing of the two/multiple images. The spectral region is divided into spatial sub bands of different scales and orientations, and within each scale a combination rule for the corresponding pixels taken from the two components is applied. Even when the combination rule is a binary selection the combined fused image may have a combination of pixel values taken from the two components at various scales since it is taken at each scale. The visual band input is given preference in low scale, large features fusion. This fusion process provides a fused image better tuned to the natural and intuitive human perception. This is necessary for pilotage and navigation under stressful conditions, while maintaining or enhancing the targeting detection and recognition performance of proven display fusion methodologies. The fusion concept was demonstrated against imagery from image intensifiers and forward looking infrared sensors currently used by the U.S. Navy for navigation and targeting. The approach is easily extendible to more than two bands.

关键词： image fusion contrast visual model night pilotage

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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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Adaptive data fusion using the expected output membership function

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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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ELIPS: toward a sensor fusion processor on a chip

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 209-219页

作者： Daud, Taher Stoica, Adrian Thomas, Tyson Li, Wei-te Fabunmi, James California Inst of Technology Pasadena United States

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the ELIPS concept targets interceptor functionality; other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an 'intelligent' processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data, three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.

关键词： Computer hardware

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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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Automated image registration using the projective transformation model and block matching feature point pair selection

Automated image registration using the projective transforma...

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

作者： Schultz, RR Alford, MG Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819431931

A subpixel-resolution image registration algorithm based on the nonlinear projective transformation model is proposed to account for camera translation, rotation, zoom, pan, and tilt. Typically, parameter estimation techniques for rigid-body transformations require the user to manually select feature point pairs between the images undergoing registration. In this research, the block matching algorithm is used to automatically select correlated feature point pairs between two images;these features are then used to calculate an iterative least squares estimate of the nonlinear projective transformation parameters. Since block matching is only capable of estimating accurate displacement vectors in image regions containing a large number of edges, inaccurate feature point pairs are statistically eliminated prior to computing the least squares parameter estimate. Convergence of the registration algorithm is generally achieved in several iterations. Simulations show that the algorithm estimates accurate integer- and subpixel-resolution registration parameters for similar sensor data sets such as intensity image sequence fi ames, as well as for dissimilar sensor images such as multimodality slices from the Visible Human Project. Through subpixel-resolution registration, integrating the registered pixels fi om a short sequence of low-resolution video fi-ames generates a high-resolution video still. Experimental results are also shown in utilizing dissimilar data registration followed by vector quantization to segment tissues from multimodality Visible Human Project image slices.

关键词： block matching image registration projective transformation sensor fusion super-resolution enhancement

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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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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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Fuzzy multisensor tracking system

Fuzzy multisensor tracking system

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

作者： Chan, Keith C.C. Lee, Vika Leung, Henry Hong Kong Polytechnic Univ Kowloon Hong Kong

Many multi-sensor target tracking systems are developed under the assumptions that data association is too complex and computational requirement is too excessive for centralized fusion approaches to be practical. In addition, it is also assumed that the noise component is relatively small, that there are no missed detection and that the scanning interval is relatively short, etc. Many multi-sensor tracking systems have been shown to be able to perform effectively when tested with simulated data generated under these assumptions. However, careful investigation into the characteristics of several sets of real data reveals that these assumptions cannot always be made validly. In this paper, we first describe the characteristics of a real multisensor tracking environment and explain why existing systems may not be able to perform their task effectively in such environment. We then present a data fusion technique that can overcome some of the weaknesses of these systems. This technique consists of three steps: (i) estimation of synchronization error using an adaptive learning approach;(ii) adjustment of measured positions of a target in case of missed detection;and (iii) prediction of the next target position using a fuzzy logic based algorithm. For performance evaluation, we tested the technique using different sets of real and simulated data. The results obtained are very satisfactory.

关键词： sensor data fusion

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