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

作者： Rao, NSV Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)0819428256

A sample from a class defined on a finite-dimensional Euclidean space and distributed according to an unknown distribution is given. We are given a set of classifiers each of which chooses a hypothesis with least misclassification error from a family of hypotheses. We address the question of choosing the classifier with the best performance guarantee versus combining the classifiers using a fuser. We first describe a fusion method based on isolation property such that the performance guarantee of the fused system is at least as good as the best of the classifiers. For a more restricted case of deterministic classes, we present a method based on error set estimation such that the performance guarantee of fusing all classifiers is at least as good as that of fusing any subset of classifiers.

关键词： classification finite sample analysis distributed detection fusion of classifiers

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MathWeb™:: A concurrent image analysis tool suite for multi-spectral data fusion

MathWeb™:: A concurrent image analysis tool suite for multi...

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

作者： Achalakul, T Haaland, PD Taylor, S Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819431931

This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect cancer cell structures. The approach combines a multi-spectral microscope with an image analysis tool suite, MathWeb. The tool suite incorporates a concurrent Principal Component Transform (PCT) that is used to fuse the multi-spectral data. This paper describes the general approach and the concurrent PCT algorithm. The algorithm is evaluated from both the perspective of image quality and performance scalability.

关键词： concurrent computing image fusion principal component transform spectral signature

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons' Gait 23

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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23rd IEEE conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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RGB-LiDAR fusion for accurate 2D and 3D object detection

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MACHINE VISION and applications 2023年第5期34卷 1-11页

作者： Mousa-Pasandi, Morteza Liu, Tianran Massoud, Yahya Laganiere, Robert Univeristy Ottawa Ottawa ON Canada

Effective detection of road objects in diverse environmental conditions is a critical requirement for autonomous driving systems. Multi-modal sensor fusion is a promising approach for improving perception, as it enables the combination of information from multiple sensor streams in order to optimize the integration of their respective data. fusion operators are employed within fully convolutional architectures to combine features derived from different modalities. In this research, we present a framework that utilizes early fusion mechanisms to train and evaluate 2D object detection algorithms. Our evaluation shows that sensor fusion outperforms RGB-only detection methods, yielding a boost of +15.07% for car detection, +10.81% for pedestrian detection, and +19.86% for cyclist detection. In our comparative study, we evaluated three arithmetic-based fusion operators and two learnable fusion operators. Furthermore, we conducted a performance comparison between early- and mid-level fusion techniques and investigated the effects of early fusion on state-of-the-art 3D object detectors. Lastly, we provide a comprehensive analysis of the computational complexity of our proposed framework, along with an ablation study.

关键词： Multimodal deep learning sensor fusion fusion operators Object detection Autonomous driving Convolutional neural networks

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sensor array calibration via tracking with the extended Kalman filter

Sensor array calibration via tracking with the extended Kalm...

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

作者： Cevher, V McClellan, JH Georgia Tech Sch ECE CSIP Atlanta GA 30332 USA

ISBN: (纸本)0819440809

Starting with a randomly distributed sensor array with unknown sensor orientations, array calibration is needed before target localization and tracking can be performed using classical triangulation methods. In this paper, we assume that the sensors are only capable of accurate direction of arrival (DOA) estimation. The calibration problem cannot be completely solved given the DOA estimates alone, since the problem is not only rotationally symmetric but also includes a range ambiguity. Our approach to calibration is based on tracking a single target moving at a constant velocity. In this case, the sensor array can be calibrated from target tracks generated by an extended Kalman filter (EKF) at each sensor. A simple algorithm based on geometrical matching of similar triangles will align the seperate tracks and determine the sensor positions and orientations relative to a reference sensor. Computer simulations show that the algorithm performs well even with noisy DOA estimates at the sensors.

关键词： tracking array calibration extended Kalman filter DOA

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Bayesian approach to sensor fusion in a multi-sensor land mine detection system

Bayesian approach to sensor fusion in a multi-sensor land mi...

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

作者： Erickson, D Kacelenga, R Palmer, D Gen Dynam Canada Ltd Calgary AB T2E 8P2 Canada

ISBN: (纸本)0819444812

In the present study, the investigation by General Dynamics Canada, formerly Computing Devices Canada, into Bayesian Inference shows improved sensor fusion of multiple scanning sensors in the detection of buried anti-tank (AT) mines. This algorithm uses statistical data taken from trials and constructs conditional probabilities for individual sensors in order to better discern landmines.

关键词： landmine detection Baye's rule Bayesian inference conditional probability minimum metal detector ground penetrating radar forward-looking infrared multi-sensor fusion

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Better-than-the-Best fusion Algorithm with Application in Human Activity Recognition

Better-than-the-Best Fusion Algorithm with Application in Hu...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Najjar, Nayeff Gupta, Shalabh Univ Connecticut Storrs CT 06269 USA

ISBN: (纸本)9781628416145

This paper introduces the Better-than-the-Best fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts;besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.

关键词： sensor fusion Information fusion Human Activity Recognition BB-Fus Decision Trees

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Quantisation for probability-level fusion on a bandwidth budget

Quantisation for probability-level fusion on a bandwidth bud...

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

作者： Black, JV Bedworth, MD Def Evaluat & Res Agcy Malvern Malvern WR14 3PS Worcs England

ISBN: (纸本)0819428256

Results are established for a simulated data fusion architecture featuring a synthetic two-class Gaussian problem, with Bayesian recognisers. The recognisers output posterior probabilities for each class. The probabilities from two or more recognisers of identical error rate are quantised using the nearest-neighbour coding rule. The coded values are decoded at a fusion centre and fused. A decision is made from the fused probabilities. The performance of the architecture is examined experimentally using code values that are uniformly distributed and code values that are produced using the Linde-Buzo-Grey (LEG) algorithm. Results are produced for two to six sensors and two to 32 code values. These results are compared to fusing probabilities represented using 32 bit floating-point numbers. Using 32 uniform or LEG-produced code values, produces results that are at most only 1% worse than fusing the uncoded probabilities.

关键词： data fusion quantisation architecture

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Using Dempster-Shafer methods for object classification in the theater ballistic missile environment

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 103-113页

作者： Coombs, Kimberly Freel, Debra Lampert, Douglas Brahm, Steven Nichols Research Huntsville United States

The Dempster Shafer (DS) Theory of Evidential Reasoning may be useful in handling issues associated with theater ballistic missile discrimination. This paper highlights the Dempster-Shafer theory and describes how thi... 详细信息

关键词： sensor data fusion

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Intelligent processing techniques for sensor fusion

Intelligent processing techniques for sensor fusion

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

作者： Byrd, K Smith, B Allen, D Morris, N Bjork, C Deal-Giblin, K Nichols Res Corp Huntsville AL 35815 USA

ISBN: (纸本)0819428256

Intelligent processing techniques which can effectively combine sensor data from disparate sensors by selecting and using only the most beneficial individual sensor data is a critical element of exoatmospheric interceptor systems. A major goal of these algorithms is to provide robust discrimination against stressing threats in poor a priori conditions, and to incorporate adaptive approaches in off-nominal conditions. This paper summarizes the intelligent processing algorithms being developed, implemented and tested to intelligently fuse data from passive infrared and active LADAR sensors at the measurement, feature and decision level. These intelligent algorithms employ dynamic selection of individual sensor features and the weighting of multiple classifier decisions to optimize performance in good a priori conditions and robustness in poor a priori conditions. Features can be dynamically selected based on an estimate of the feature confidence which is determined from feature quality and weighting terms derived from the quality of sensor data and expected phenomenology. Multiple classifiers are employed which use both fuzzy logic and knowledge based approaches to fuse the sensor data and to provide a target lethality estimate. Target designation decisions can be made by fusing weighted individual classifier decisions whose output contains an estimate of the confidence of 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 or to request additional sensor data on specific objects that have not been confidently identified as being lethal or non-lethal, The algorithms are implemented in C within a graphic user interface framework. Dynamic memory allocation and the sequential implementation of the feature algorithms are employed. The baseline set of fused sensor discrimination algorithms with intelligent processing are described in this paper. Example results from

关键词： sensor fusion intelligent processing discrimination feature extraction infrared laser radar fuzzy logic

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