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sensor fusion options for ballistic missile defense interceptor applications

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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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 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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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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Study of a modified Dempster-Shafer approach using an expected utility interval decision rule

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proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 34-42页

作者： Cheaito, Ali Lecours, Michel Bosse, Eloi Universite Laval Canada

The Combination operation of the conventional Dempster-Shafer algorithm has a tendency to increase exponentially the number of propositions involved in bodies of evidence by creating new ones. The aim of this paper is to explore a 'modified Dempster-Shafer' approach of fusing identity declarations emanating from different sources which include a number of radars, IFF and ESM systems in order to limit the explosion of the number of propositions. We use a non-ad hoc decision rule based on the expected utility interval (EUI) to select the most probable object in a comprehensive Platform Data Base (PDB) containing all the possible identity values that a potential target may take. We study the effect of the redistribution of the confidence levels of the eliminated propositions which otherwise overload the real-time data fusion system; these eliminated confidence levels can in particular be assigned to ignorance, or uniformly added to the remaining propositions and the ignorance. A scenario has been selected to demonstrate the performance of our modified Dempster-Shafer method of evidential reasoning.

关键词： sensor data fusion

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ELIPS: Toward a sensor fusion processor on a chip

ELIPS: Toward a sensor fusion processor on a chip

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

作者： Daud, T Stoica, A Thomas, T Li, WT Fabunmi, J CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)0819431931

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 FLIPS 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： sensor fusion hardware sensor fusion processor fuzzy expert system neural networks reconfigurable hardware

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Bio-fusion for intelligent system control

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proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 410-417页

作者： Norseen, John D. Kropotov, Juri D. Kremen, Inna Z. Lockheed Martin Aeronautical Co

We present a realistic neural network - the canonical cortical module - built on basic principles of cortical organization. These principles are: opponent cells principle, assembly principle, canonical cortical circuit principle and modular principle. When applied to visual images, the network explains orientational and spatial frequency filtering functions of neurons in the striate cortex. Two patterns of joint distribution of opponent cells in the inhibitory cortical layer are presented: pinwheel and circular. These two patterns provide two Gestalt descriptions of local (within the frames of one module) visual image: circle-ness and cross-ness. These modules were shown to have a power for shape detection and texture discrimination. They also provide an enhancement of signal-to-noise ratio of input images. Being modality independent, the canonical cortical module seems to be good tool for bio-fusion for intelligent system control.

关键词： Neural networks

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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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PCA and wavelet transform for fusing panchromatic and multi-spectral images

PCA and wavelet transform for fusing panchromatic and multi-...

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

作者： Li, J Zhou, YQ Li, DR Wuhan Tech Univ Surveying & Mapping LIESMARS Wuhan 430070 Peoples R China

ISBN: (纸本)0819431931

Facing the increasing availability of remote sensing imagery, the compression of information and the combining of multi-spectral and multi-sensor image data are becoming of greater importance. This paper presents a new image fusion scheme based on PCA and multi-resolution analysis of wavelet theory for fusing high-resolution panchromatic and multi-spectral images. It is done in two ways: a). By replacing some wavelet coefficients of k-principal components by the corresponding coefficients of the high-resolution panchromatic image;b). By adding the wavelet coefficients of the high-resolution panchromatic image directly to k-principal components. The proposal approach is used to fuse the SPOT panchromatic and Landsat (TM) multi-spectral images. Experimental results demonstrate that the proposal approach can not only preserve all the spectral characteristics of the multi-spectral images, but can also improve their definition and spatial quality. Compared with the PCA fusion method, the proposal scheme is much better and possesses more capable of adaptability.

关键词： PCA the wavelet planes image merging substitution fusion additive fusion

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sensor management in an ASW data fusion system

Sensor management in an ASW data fusion system

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

作者： Penny, DE Def Evaluat & Res Agcy Malvern WR14 3PS Worcs England

ISBN: (纸本)0819431931

The Multi-sensor fusion Management (MSFM) algorithm positions multiple, detection-only, passive sensors in a two-dimensional plane to optimise the fused probability of detection using a simple decision fusion method, previously the MSFM algorithm was evaluated on two synthetic problem domains comprising of both static and moving targets(1). In the original formulation the probability distribution of the target location was modelled using a non-parametric approach. The logarithm of the fused detection probability was used as a criterion function for the optimisation of the sensor positions. This optimisation used a straightforward gradient ascent approach, which occasionally found local optima. Following the placement optimisation the sensors were deployed and the individual sensor detections combined using a logical OR fusion rule. The target location distribution could then be updated using the method of sampling, importance re-sampling (SIR). In the current work the algorithm is extended to admit a richer variety of behaviour. More realistic sensor characteristic models are used which include detection-plus-bearing sensors and false alarm probabilities commensurate with actual sonar sensor systems. In this paper the performance of the updated MSFM algorithm is illustrated on a realistic anti-submarine warfare (ASW) application(2) in which the placement of the sensors is carried out incrementally, allowing for the optimisation of both the location and the number of sensors to be deployed.

关键词： sensor fusion resource allocation anti-submarine warfare (ASW)

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