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Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

Performance evaluation of neural network algorithms for mult...

引用

International conference on Neural Networks

作者： L.M. Patnaik H. Nair V. Abraham G. Raghavendra S.K. Singh R. Srinivasan K. Ramchand Microprocessor Applications Laboratory Indian Institute of Science Bangalore India Indian Institute of Science Bangalore Karnataka IN Center for Airborne Systems DRDO Bangalore India

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques.

关键词： Neural networks Intelligent networks Radar tracking sensor fusion Airborne radar Azimuth Backpropagation algorithms Aircraft Target tracking Radar applications

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Maximizing multi-sensor system dependability

Maximizing multi-sensor system dependability

引用

International conference on Multisensor fusion and Integration for Intelligent Systems (MFI)

作者： R.R. Brooks S.S. Iyengar Communications Science and Technology Center California State University Seaside CA USA Department of Computer Science Louisiana State University Baton Rouge LA USA

This paper considers distributed sensor systems and finds redundant configurations which maximize dependability while insuring the system remains within cost or weight constraints. Given different sensor modules which fulfil the system's operational requirements but have different dependability and cost parameters, efficient methods are used to find maximum dependability configurations. These methods limit the search to a constrained subspace of the problem space. It is shown that this region must contain the optimal configuration. Three heuristics: genetic algorithms, simulated annealing and tabu search are used. Experimental results are presented with dependability gains of between 10 and 15%. These test cases compare results from all methods and verify that in most cases the simulated annealing heuristic provides the best solutions.

关键词： sensor systems Intelligent sensors sensor fusion Costs Redundancy Simulated annealing Nuclear magnetic resonance sensor systems and applications Communications technology Computer science

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Image processing techniques for laser images

Image processing techniques for laser images

引用

Canadian conference on Electrical and Computer Engineering (CCECE)

作者： I. Saban S. Faibish RAFAEL Advanced Defense Systems Limited Haifa Israel Robotics and Automation Laboratory University of Toronto Toronto ONT Canada

This paper presents the signal processing method of laser images used for detection of small collinear obstacles in helicopter airborne applications. It is very difficult, if not almost impossible, for a regular passive imaging sensor based on CCD detectors to detect such small, remote objects. It was proved in the literature that the fusion of range and intensity data is needed for such a task. The paper presents a new and improved algorithm for real time image processing which enable detection of small tiny objects from sparse data, from a fast moving platform.

关键词： Image processing Cables Laser radar Object detection Signal processing algorithms Image edge detection Laser fusion Layout Signal processing Helicopters

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''Object classification by using textural and geometric classificators''

''Object classification by using textural and geometric clas...

引用

Machine Vision applications, architectures, and Systems Integration IV conference

作者： Foertsch, D Weller, U VITRON GMBH D-65189 WIESBADENGERMANY

ISBN: (纸本)0819419613

This paper presents a typical industrial application of machine vision in order to classify and select different types of cylindrical steel bars in combination with direct process control. The main classification algorithm consists of a combination of several routines, using different image processing methods. On the one hand a textural approach, using first and second order statistics, is used. Typical histogram data in addition with gray level dependence matrices give some textural classification criteria. On the other hand the search and utilization of geometric criteria supply additional features for classification. Several contour measurement routines deliver a set of additional information about the examined bar. The paper offers details about the used classification algorithms. Furthermore it deals with experimental results such as velocity and rate of selection success.

关键词： visual sensor system process control cylindrical steel bars feature extraction classification algorithms

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Data fusion and nonlinear tracking filter implementation using multilayer networks

Data fusion and nonlinear tracking filter implementation usi...

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Proceedings of the 1995 IEEE International conference on Neural Networks. Part 1 (of 6)

作者： Sundareshan, Malur K. Amoozegar, Farid Univ of Arizona Tucson United States

One of the capabilities of multilayer neural nets that has not received much attention is the ability to efficiently fuse information of different forms for facilitating intelligent decision-making. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the currently popular efforts at designing nonlinear estimation algorithms for tracking applications, the principal one being the reduction of mathematical and computational complexities.

关键词： Neural networks

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Proceedings of the 1995 17th Annual Custom Integrated Circuits conference

Proceedings of the 1995 17th Annual Custom Integrated Circui...

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Proceedings of the 1995 17th Annual Custom Integrated Circuits conference

The proceedings contains 144 papers from the 1995 IEEE Custom Integrated Circuits conference. Topics discussed include: gate array technologies and applications;design methodology issues;fabrication and packaging technology;reliability by design;analog communication circuits;mixed signal and switching noise simulation;specialized memory macros and architectures;application-specific digital signal processing;data conversion;device modeling and simulation algorithms;layout analysis and optimization;consumer applications;CMOS for wireless communications;sensor interface and image processing circuits;library development and interconnect modeling;MPEG video and audio ICs;ATM and broadband communications;chip and module layout synthesis.

关键词： Application specific integrated circuits

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Neural networks and flight control: Overview of capabilities and emerging applications

Neural networks and flight control: Overview of capabilities...

引用

Guidance, Navigation, and Control conference, 1995

作者： Werbos, Paul J. National Science Foundation ArlingtonVA United States

The artificial network (ANN) field is highly complex. It contains designs which parallel other algorithms to perform tasks like image and speech preprocessing, static function minimization, pattern recognition, system identification, sensor fusion, control, feature extraction, clustering and data compression. ANNs are a subset of mathematical algorithms, not an alternative to them;however, they are a useful subset, allowing the use of new chips with enormous throughput per dollar, and permitting system development based on learning instead of tweaking -which can reduce development times and costs substantially. Many ANN applications are motivated by new universal approximation theorems (showing that some ANNs work better than Taylor series), by ease of use, and by increasingly serious efforts to replicate brain-like intelligence. Neurocontrol contains designs to perform 3 basic tasks: (1) cloning experts or large computer programs, based on what the experts do, not what they say (2) tracking set points or desired trajectories, as in adaptive control;(3) optimization over time. Learning-based optimization - accounting for delayed impacts - may be crucial to the improved mass ratios needed for hypersonic flight, to optimizing the recovery of damaged aircraft, to improved missile interception, and to overcoming several sources of instability in conventional controllers. © 1995 American Institute of Aeronautics and Astronautics Inc, AIAA. All right reserved.

关键词： Speech recognition

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Data fusion and nonlinear tracking filter implementation using multilayer networks

Data fusion and nonlinear tracking filter implementation usi...

引用

International conference on Neural Networks

作者： M.K. Sundareshan F. Amoozegar Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

One of the capabilities of multilayer neural nets that has not received much attention is the ability to efficiently fuse information of different forms for facilitating intelligent decision-making. In this paper the authors describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the currently popular efforts at designing nonlinear estimation algorithms for tracking applications, the principal one being the reduction of mathematical and computational complexities.

关键词： Filters Target tracking Neural networks Multi-layer neural network Fuses Decision making Vehicles Surveillance Feature extraction Data mining

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A robust approach to decentralized CFAR detection in spiky clutter

A robust approach to decentralized CFAR detection in spiky c...

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European conference on Microwave

作者： Fulvio Gini Fabrizio Lombardini Lucio Verrazzani Department of Information Engineering University of Pisa Pisa Italy

In this paper we consider the problem of decentralized constant-false-alarm-rate (CFAR) detection in a distributed multiradar system, by means of an approach based on the concept of robustness. State-of-the-art biparametric CFAR algorithms are computationally demanding and lossy, so the question that arises is: Are they really necessary? We show that the answer is no, or rather not always. A new simple monoparametric CFAR detector with postdetection integration is proposed, that, in spite of the single parameter estimation, allows to obtain a false alarm probability at the fusion center robust against changes of the degree of clutter spikiness. The robustness is obtained by a joint action: multisensorial integration, local temporal integration, in conjunction with a specific monoparametric estimator. After an analysis of the robust algorithm, we determine the detection performance of the multisensor network and present a comparison with a decentralized system employing biparametric algorithms.

关键词： Robustness Radar detection Detectors Testing Radar applications sensor systems Shape Radar clutter Probability density function Topology

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DETERMINATION OF FUZZY DECISION fusion SYSTEM PARAMETERS BY GENETIC algorithms 5

DETERMINATION OF FUZZY DECISION FUSION SYSTEM PARAMETERS BY ...

引用

5th International conference on applications of Artificial Neural Networks

作者： LOSKIEWICZBUCZAK, A UHRIG, RE UNIV TENNESSEE DEPT NUCL ENGNKNOXVILLETN 37996

ISBN: (纸本)0819415472

This paper describes a decision fusion method based on fuzzy logic and genetic algorithms. For the fusion process the generalized mean aggregation connective is used. The optimal parameters of the generalized mean are found by a genetic algorithm both with elitist and nonelitist strategy. The results of both strategies are compared. The decision fusion method proposed is tested on a vibration monitoring problem. The decisions from multiple sensors to be fused are obtained by neural networks. First vibration spectra are compressed by recirculation networks. Next classification of compressed signatures is performed for each sensor separately by backpropagation networks. The output of backpropagation networks is the input to the fuzzy fusion center performing the generalized mean operation.

关键词： sensors Genetic algorithms Fuzzy logic Data fusion Information fusion Neural networks sensor fusion Fuzzy systems sensor performance Classification systems

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