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Conference on sensor fusion - architectures, algorithms, and applications

作者： Williams, A Pachowicz, P Ronk, L SAIC Arlington VA 22203 4001 N. Fairfax Dr. United States George Mason University Systems Engineering Dept. Fairfax VA 22030 4400 University Dr. United States

ISBN: (纸本)081942482X

In this paper, we discuss a new fusion architecture, including some preliminary results on field data. The architecture consists of a new decision level fusion algorithm, the Piecewise Level fusion Algorithm (PLFA), integrated with a new expert system based user assistant that adjusts PLFA parameters to optimize for a user desired classification performance. This architecture is applicable for both multisensor and multilook fusion. The user specifies classification performance by inputting entries for a desired confusion matrix at the fusion center. The intelligent assistant suggests input alternatives to reach the performance goal based on previously supplied user inputs and on performance specifications of the individual sensors. If deadlock results, i.e., the goal is not attainable because of conflicting user inputs, the assistant Rill inform the user. As the user and assistant interact, the assistant calculates the parameters necessary to automatically adjust the PLFA for the required performance. These parameters and calculations are hidden from the user. That is, the architecture is designed so that user inputs are intuitive for an unskilled operator. The implementation of this adaptable fusion architecture is due to the relatively simple structure of the PLFA and the expert system heuristic rules. We briefly describe the PLFA structure and operation, illustrate some expert system rules, and discuss preliminary performance of the entire architecture, including a sample dialog between the user and the intelligent assistant. We conclude this paper with a discussion of future extensions to this architecture that include replacing human interactions with dynamic learning techniques.

关键词： multi-sensor fusion multi-look fusion expert assistant decision level fusion adaptive systems fusion architecture

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Distributed detection with multiple sensors .1. Fundamentals

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PROCEEDINGS OF THE IEEE 1997年第1期85卷 54-63页

作者： viswanathan, R varshney, PK SYRACUSE UNIV DEPT COMP SCI & ELECT ENGN SYRACUSE NY 13244 USA

In this paper, basic results on distributed detection are reviewed. In particular, we consider the parallel and the serial architectures in some detail and discuss the decision rules obtained from their optimization based on the Neyman-Pearson (NP) criterion and the Bayes formulation. For conditionally independent sensor observations, the optimality of the likelihood ratio test (LRT) at the sensors is established. General comments on several important issues are made including the computational complexity of obtaining the optimal solutions, the design of detection networks with more general topologies, and applications to different areas.

关键词： sensor fusion Intelligent sensors Radar detection Infrared detectors Sonar detection Signal processing algorithms Testing Network topology Signal processing Costs

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Gyroscopic data fusion via a quaternion based complementary filter

Gyroscopic data fusion via a quaternion based complementary ...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Smith, R Frost, A Probert, P Oxford Univ. (UK) and Silsoe Research Institute (United Kingdom) Silsoe Research Institute (United Kingdom) Oxford Univ. (United Kingdom)

ISBN: (纸本)081942482X

We present an application of a complementary filter system to the attitude determination of a remotely operated underwater vehicle (ROv). The main contribution of this paper is to combine existing complementary filter theory with quaternion attitude representation. The combination allows accurate attitude determination by a real-time system using low cost sensors. We fuse the estimate from an Extended Kalman Filter (EKF) with the output from a set of vibrating structure rate gyroscopes. The EKF supplies high-quality low frequency information, the gyroscopes supply corresponding high frequency information. The attitude is described via a quaternion representation. We discuss how the use of quaternions is beneficial for estimator design due to the low computational burden, and lack of discontinuities and singularities. The EKF combines the output from two inclinometers and a magnetometer with a vehicle process model. The EKF assumes that sensor and process noise is broadband and that the process model captures all the important dynamics. An underwater vehicle is capable of rapid rotations, which are difficult to model, and would require computationally unattainable update rates to track effectively. We develop a filter, which uses the difference between the EKF and gyroscopic attitude estimates (an indirect filter) to correct for drift in the gyroscopic attitude estimate. We develop first a feedforward and then a feedback filter. The simplicity of the indirect filter permits very fast update rates, so the system may follow rapid vehicle rotations. We discuss the real-time implementation of the estimator on a Transputer based system mounted within a small ROv. We present experimental results showing the system performance of the combined filter system.

关键词： gyroscopes indirect filter data fusion quaternion estimation

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Novel approach to multispectral blind image fusion

Novel approach to multispectral blind image fusion

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sensor fusion: architectures, algorithms, and applications

作者： Kundur, D. Hatzinakos, D. Leung, H. Department of Electrical and Computer Engineering University of Toronto Toronto ON M5S 3G4 Canada Department of National Defense Defense Research Establishment Ottawa Building 29 3701 Carling Avenue Ottawa ON K1A 0Z4 Canada

In this paper we propose a robust method of data fusion for the classification of multispectral images. The approach is novel in that it attempts to remove blurring of the images in conjunction with fusing the data. This produces a more robust and accurate overall classification scheme. The approach is applicable to situations in which registered multispectral images of the same scene are available. The novel scheme is comprised of three main stages. The first stage involves the blind restoration of the degraded multispectral images to combat blurring effects. The results are fused in the second stage with a statistical classification method which performs both pixel-level and intermediate-level classification. The classification output is then passed through a final stage which provides a relative measure of the success of the classification method. This information is fed back to the first stage to improve the reliability of the restoration method. The performance of the proposed scheme is demonstrated by applying the technique to simulated and real photographic data. The simulation results demonstrate the potential of the method for robust classification of degraded data. ©2004 Copyright SPIE - The International Society for Optical Engineering.

关键词： Image fusion

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sensor fusion: Architecture, algorithms, and applications

Sensor Fusion: Architecture, Algorithms, and Applications

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1997年

作者： Belur v Dasarathy

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Multi-sensor integration method of signals in a metal cutting operation via application of multi-layer perceptron neural networks

Multi-sensor integration method of signals in a metal cuttin...

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Proceedings of the 1997 5th International Conference on Artificial Neural Networks

作者： Dimla Jr., D.E. Lister, P.M. Leighton, N.J. Univ of Wolverhampton Wolverhampton United Kingdom

The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such a manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable Tool Condition Monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realized through traditional tool changing philosophies. Unfortunately, the neural network algorithms used have been complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple Multi-layer Perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90% was attainable.

关键词： sensor data fusion

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A multi-sensor integration method of signals in a metal cutting operation via application of multilayer perceptron neural networks

A multi-sensor integration method of signals in a metal cutt...

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Fifth International Conference on Artificial Neural Networks (Conf. Publ. No. 440)

作者： D.E. Dimia P.M. Lister N.J. Leighton Eng. Res. Group Univ. of Wolverhampton UK Engineering Research Group SEBE University of Wolverhampton UK

The potential application of neural networks in manufacturing scenarios is increasingly becoming feasible. Typical of such manufacturing scenario is the integration of metal cutting sensor signals in pursuance of reliable tool condition monitoring (TCM) system. Successful application of this method of sensor integration could save downtime and costs, that would otherwise not have been realised through traditional tool changing philosophies. Unfortunately, the network algorithms used have complicated, requiring detailed sensor signal pre-processing. Partly as a consequence, developed systems have found very limited applications to-date. In this paper, the authors present a simple sensor fusion method via the neural networks approach to the TCM problem. Turning tests were conducted from which the static cutting force, dynamic cutting force and the vibration signature were recorded. The obtained data was used to investigate the classification capability of simple multilayer perceptron (MLP) neural network architectures to the detection of tool wear. Obtained results showed classification accuracy of well over 90%.

关键词： Neural networks

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Surface segmentation of laser range images for automated facility mapping

Surface segmentation of laser range images for automated fac...

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Conference on Machine vision applications, architectures, and Systems Integration v

作者： Pinheiro, RJ Crane, CD Tulenko, JS Haddox, D Univ. of Florida (United States)

ISBN: (纸本)0819423106

A method for modeling a hazardous environment automatically, for real time task planning, using laser range images of multiple partial views of a single work space scene, is presented. viewpoint invariant properties of differential-geometric shape descriptors like the Mean curvature and the Gaussian curvature are utilized to classify a pre-smoothed laser range image into one of eight basic surface types. Connected components of these classified pixels, that satisfy specific planarity constraints, are clustered into planar regions. Selected image processing techniques are applied to the planar regions in order to extract their critical features, and to synthesize those polygons, with normals approximately orthogonal to the sensor view-axis. Detailed shape of the objects in the scene develop through view integration of multiple partial views of the objects in the scene.

关键词： facility mapping mobile mapping modeling segmentation data fusion range images feature extraction world modeling site characterization laser range imagery

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Model-based matching for precise sensor fusion

Model-based matching for precise sensor fusion

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Signal Processing, sensor fusion, and Target Recognition v

作者： Ely, Richard W. Mueller, Walter J. Texas Instruments Inc. Lewisville TX USA

ISBN: (纸本)0819421367

Image sensor fusion techniques such as model supported exploitation require accurate image support data to ensure accurate image-to-image or image-to-site model registration. Photogrammetric control of imagery is currently a time consuming process but necessary for these applications. For areas which are exploited repeatedly, a database of control features can be built and used by an automatic algorithm to control new images. The algorithm automatically locates the control features in the images and uses the resulting correspondences to perform a rigorous adjustment of the image support data which accurately ties the image to ground coordinates. Other source data referenced to ground coordinates is by association registered to the imagery and can be used to support sensor/data fusion algorithms. The approach for creating, maintaining and applying the control features is discussed.

关键词： sensors

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Parallel algorithms for m-out-of-n threshold voting

Parallel algorithms for m-out-of-n threshold voting

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IEEE International Conference on algorithms and architectures for Parallel Processing (ICAP)

作者： B. Parhami Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

voting on large collections of input objects is becoming increasingly important in data fusion, signal and image processing, and distributed computing. To achieve high speed in voting, the multiple processing resources typically available in such applications should be utilized; hence the need for parallel voting algorithms. We develop efficient parallel algorithms for threshold voting which generalize and extend previous work on both sequential threshold voting and parallel majority voting. We show how a well-known O(n)-time sequential algorithm for m-out-of-n voting can be parallelized through 1 simple divide-and-conquer strategy. When m=/spl theta/(n), the resulting algorithm has O(log/sup 2/ n) time complexity on PRAM and hypercube computers and optimal O(n/sup 1/k/) complexity on a k-dimensional mesh-connected architecture. We also analyze the time complexity of the algorithm in the case of m=o(n) and for certain weighted threshold voting schemes.

关键词： Parallel algorithms voting Hardware Distributed computing Application software Concurrent computing Diversity reception sensor fusion Phase change random access memory Hypercubes

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