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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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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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Decision-level data fusion

Decision-level data fusion

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作者： Zhu, Xiaoxun Drexel University

学位级别：Ph.D.

Research in data fusion is motivated by the potential for significant enhancement in multisensor and multi-algorithm decision making. Developed initially for radar applications, data fusion is now studied for general-purpose signal processing, diversity in communication, and high performance computing. In part I of this dissertation, we provide an overview of sensor fusion, focusing on a specific application--mobile robot navigation. We review the main techniques for sensor fusion in robot navigation, emphasizing algorithms for robot self location. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior-based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks. In part ii, we investigate the parallel decision fusion architecture whose necessity was established in part I. This architecture comprises N local detectors (LDs) and a decision fusion center (DFC). The local detectors observe a common volume of surveillance. The $i\sp{th}$ detector uses its observation vector, $z\sb{i}\in R\sp{M},$ in a local decision rule $f\sb{i}:R\sp{M}\to\{{-}1,1\}:z\sb{i}\to u\sb{i},$ to determine whether to accept the null hypothesis $H\sb0\ (u\sb{i}={-}1),$ or the alternative hypothesis $H\sb1\ (u\sb{i}=1).$ The local observations are assumed to be statistically independent, conditioned on the hypothesis. The local decision vector $\{u\sb{i}\}\sbsp{i=1}{N}$ is then transmitted to the DFC which uses a global decision rule $f\sb0:\{{-}1,1\}\sp{N}\to\{{-}1,1\}:\{u\sb{i}\}\sbsp{i=1}{N}\to u\sb0,$ to generate the global decision, $u\sb0.$ We extend the existing optimal design in two directions: (1) Adaptive decision fusion: when a priori probabilities and sensor characteristics are unknown, we devise an algorithm that learns the decision rule at the DFC on line. (2) M-ary hypothesis testing: when the loca

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Estimating bidirectional reflectance parameters by forward modeling and statistical inversion of remotely sensed data

Estimating bidirectional reflectance parameters by forward m...

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Statistical and Stochastic Methods in Image Processing ii

作者： Fletcher, Robin P. Grubb, Howard J. Godsalve, Christopher Department of Applied Statistics University of Reading Reading RG6 6FN United Kingdom Environmental Systems Science Centre University of Reading Reading RG6 6AB United Kingdom

We discuss the estimation of ground reflectance from remotely-sensed measurements made by a satellite-borne instrument. The particular sensor under study includes a spatially-varying weighting of the ground values within overlapping, large, low-resolution image pixels. Thus the problem is one of estimating sub-pixel information from a low resolution observation. The observation process involves interaction with an unknown atmosphere, realistic modeling of which requires sophisticated and computationally expensive algorithms. These make it difficult to specify likelihood functions for use in a fully Bayesian approach to the inversion and so we work with atmospherically-corrected data in a penalized least-squares framework. We formulate realistic physical models for the observation process which can then be inverted using a forward modeling approach. This can be solved by using a stochastic optimization algorithm on a suitably chosen energy function, which regularizes the accuracy of reconstruction of the observed satellite data with our prior beliefs about spatial smoothness of the ground reflectance. A further complication is introduced as the sensor views the same ground point from two viewing angles at closely-spaced times. As the surface property of reflectance intrinsically varies with viewing angle, depending on the vegetation or ground cover, these two views allow us to jointly estimate two parameters of a suitable physical model for this variation. From this we can also derive other functionals of interest to environmental applications. This estimation can be considered as a form of image fusion, via the forward model of the observation process. We consider practical aspects of the smoothness priors, the forward model and its implications for the design of an energy function and stochastic inversion algorithms in this application. We compare their performance with an existing method, on some synthetic data, generated with the important sensor properties. We d

关键词： Image 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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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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Multisensor data fusion for obstacle tracking using neuro-fuzzy estimation algorithms

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Proceedings of SPIE - The International Society for Optical Engineering 1994年 2233卷 112-123页

作者： Doyle, Rory S. Harris, C.J. Univ. of Southampton Highfield Southampton UK

ISBN: (纸本)0819415375;9780819415370

The problem addressed in this paper is that of estimating the tracks of dynamic obstacles in the environment of a helicopter operating in hazardous conditions. Fuzzy logic and neural networks have shown their strength in recent years in the solutions to non-linear problems. The aim of this paper is to present neuro-fuzzy data fusion algorithms which can be used to fuse information provided by multiple spatially separate sensors engaged in the tracking of obstacles whose dynamics are a priori unknown.

关键词： sensor data fusion

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Multisensor data fusion for obstacle tracking using neuro-fuzzy estimation algorithms

Multisensor data fusion for obstacle tracking using neuro-fu...

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sensor fusion and Aerospace applications ii

作者： Doyle, Rory S. Harris, C.J. Univ. of Southampton Highfield Southampton UK

ISBN: (纸本)0819415375

关键词： sensor data fusion

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IMAGE-BASED TARGET TRACKING - fusion ARCHITECTURE AND sensor MANAGEMENT 2

IMAGE-BASED TARGET TRACKING - FUSION ARCHITECTURE AND SENSOR...

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CONF ON SIGNAL PROCESSING, sensor fusion, AND TARGET RECOGNITION 2

作者： HERSEN, B LALO, A LANEUVILLE, D MARITON, M MATRA MS2i (France)

ISBN: (纸本)0819411914

Following the acceptance of the linear Gauss Markov paradigm pioneered by Kalman, the engineering practice for the design of target tracking applications had been maturing over the last two decades. In recent years however two emerging facts have called for a renewed attention from the research community: (1) the generalization of multiple sensor architectures, motivated by higher requirements in terms of target description and robustness to electronic warfare, and (2) the availability of affordable imaging sensors, following progress in infrared detectors technology. The purpose of this communication is to report on some recent work addressing the issues raised by these two new aspects of tracking application design. Ideas are illustrated using an air defense scenario.

关键词： sensors Optical sensors Detection and tracking algorithms Environmental sensing Image processing Defense and security Image fusion Image sensors Imaging systems sensor fusion

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