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检索条件"任意字段=Conference on Sensor Fusion: Architectures, Algorithms, and Applications IV"

共 329 条记录，以下是161-170 订阅

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Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring

Adaptive local fusion systems for novelty detection and diag...

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

作者： Taylor, O MacIntyre, J Univ Sunderland Sch Comp & Informat Sci Ctr Adapt Syst Sunderland SR6 0DD England

ISBN: (纸本)0819428256

This paper describes the application of Kohonen Self Organising Maps in a dynamic machine condition monitoring application which learns fault conditions over time. The authors describe the implementation of a novelty detection and adaptive diagnostic system which forms a modular component of a larger on-line condition monitoring system. NEURAL-MAINE, a project under the European Union's EUREKA programme, aims to advance on-line condition monitoring applications by the use of neural networks, data fusion and multiple sensor technology. NEURAL-MAINE aims to implement a system on two levels: at the first,, Local fusion Systems (LFS) are used to model individual machine components, such as high pressure turbines;at the second, a larger 'Overseer' system sits above the LFS and takes in their input, as well as plant operating parameters, in order to give a global view of the condition of the plant. This paper describes the implementation and testing of the two major components of the Local fusion System, namely the novelty detection and the adaptive diagnostic systems. Novelty detection works by using a Kohonen-based neural network(1) to learn the normal operating state of the component that is being modeled, with new values being passed into the network to see if it is similar to the learned normal states of operation. If the new fused sensor values are unlike the values which the Kohonen neural network represents, this is nagged as novel, and the values are passed to the adaptive diagnostic system. The adaptive diagnostic system takes as its input the values that the novelty detection system identified as novel and passes these into the Kohonen-based diagnostic networks. If the diagnostic networks recognize the values then a local diagnosis is given and passed to the Overseer system. If the diagnostic networks do not recognize the values, then these patterns are dynamically learned as the new fault condition occurs, and the diagnostic networks are updated. This system a

关键词： neural networks data fusion novelty detection adaptive diagnostic system

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Multi-sensor feature fusion methods and results

Multi-sensor feature fusion methods and results

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

作者： Heagy, D Barnes, R Bechhoefer, E Mitre Corp MS W967 Mclean VA 22101 USA

ISBN: (纸本)0819440809

Analysts responsible for supporting time dominated threat decisions are faced with a growing volume of sensor data. Most efforts to increase discrimination among targets using multiple types of sensors encounter the same problems: sensor data are received in large volumes. sensor data are highly variable. Signature features are represented by many dimensions. Feature values are intercorrelated, random or not related to target differences. Decision rules for classifying new target data are difficult to define. This paper describes a new methodology for solving several problems: selecting signature features, reducing variability, increasing discrimination accuracy, and developing decision rules for classifying new target signatures. The results from using a combination of exploratory and multivariate statistical techniques show potential improvements over the traditional Dempster-Shafer approach. This project uses data from operational prototype sensors and vehicles of interest for threat analysis. Acoustic and seismic sensor data came from an unattended ground sensor and three military vehicles. Although the resulting algorithms are specific to the data set, the data screening and fusion methods tested in this project may be useful with other types of sensor and target data.

关键词： feature fusion multisensor classification data screening signal processing pattern recognition feature templates multivariate statistics Dempster-Shafer threat analysis

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Distributed air-to-ground targeting

Distributed air-to-ground targeting

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

作者： Ralph, JF Smith, MI Bernhardt, M West, CE Angell, CR Sims, SW Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)0819444812

This paper examines the requirement for accurate estimates of the statistical correlations between measurements in a distributed air-to-ground targeting system. The study uses results from a distributed multi-platform targeting simulation based on a level-1 data fusion system to assess the extent to which correlated measurements can degrade system performance, and the degree to which these effects need to be included to obtain a required level of accuracy. The data fusion environment described in the paper incorporates a range of target tracking and data association algorithms, including several variants of the standard Kalman filter, probabilistic association techniques and Reid's multiple hypothesis tracker. A variety of decentralized architectures are supported, allowing comparison with the performance of equivalent centralized systems. In the analysis, consideration is given to constraints on the computational complexities of the fusion system, and the availability of estimates of the measurement correlations and platform-dependent biases. Particular emphasis is placed on the localisation accuracy achieved by different algorithmic approaches and the robustness of the system to errors in the estimated covariance matrices.

关键词： targeting object localisation data fusion

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Signal-to-noise ratio for cross-sensor fusion approach

Signal-to-noise ratio for cross-sensor fusion approach

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conference on Multisensor, Multisource Information fusion

作者： Kozaitis, S. P. Ouendeno, M. Florida Inst Technol Dept Elect & Comp Engn 150 W Univ Blvd Melbourne FL 32901 USA

We determined the signal-to-noise ratios for an image fusion approach that is suitable for application to systems with disparate sensors. We found there was reconstruction error present when the forward and reverse tr... 详细信息

ISBN: (纸本)9780819466938

关键词： image fusion image transform signal-to-noise ratio wavelet wavelet transform

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

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IEEE Intelligent Transportation Systems conference (IEEE-ITSC)

作者： El Madawi, Khaled Rashed, Hazem El Sallab, Ahmad Nasr, Omar Kamel, Hanan Yogamani, Senthil Valeo R&D Cairo Cairo Egypt Cairo Univ Giza Egypt Valeo Vis Syst Galway Ireland

ISBN: (纸本)9781538670248

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.

关键词： Semantic Segmentation

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Target detection and recognition using Markov modeling and probability updating

Target detection and recognition using Markov modeling and p...

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sensor fusion: architectures, algorithms, and applications iv

作者： Kelly, Patrick A. Derin, Haluk Zhao, Renjian Univ of Massachusetts Amherst United States

While exact methods (e.g., jump-diffusion algorithms) for performing maximum a posteriori (MAP) target detection and recognition can be very complex and computationally expensive, it is often not clear how to develop effective and less complex suboptimal methods. Also, MAP algorithms typically generate hard decisions, but for fusion applications it would often be more desirable to have probabilities or confidence levels for a range of alternatives. In this paper, we consider the application of a framework called probability propagation in Bayesian networks. This framework organizes computations for iterated approximations to posterior probabilities, and has been used recently by communications researchers to derive very effective iterative decoding algorithm. In this paper, we develop a Bayesian network model for the problem of target detection and recognition, and use it in conjunction with Markov models for target regions to derive a probability propagation algorithm for estimating target shape and label probabilities.

关键词： sensor data fusion

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Multi-level image fusion

Multi-level image fusion

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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Petrovic, V Orasys MV YU-11077 Novi Beograd Serbia Yugoslavia

ISBN: (纸本)0819449598

Signal-level image fusion has in recent years established itself as a useful tool for dealing with vast amounts of image data obtained by disparate sensors. In many modem multisensor systems, fusion algorithms significantly reduce the amount of raw data that needs to be presented or processed without loss of information content as well as provide an effective way of information integration. One of the most useful and widespread applications of signal-level image fusion is for display purposes. Fused images provide the observer with a more reliable and more complete representation of the scene than would be obtained through single sensor display configurations. In recent years, a plethora of algorithms that deal with the problem of fusion for display has been proposed. However, almost all are based on relatively basic processing techniques and do not consider information from higher levels of abstraction. As some recent studies have shown this does not always satisfy the complex demands of a human observer and a more subjectively meaningful approach is required. This paper presents a fusion framework based on the idea that subjectively relevant fusion could be achieved if information at higher levels of abstraction such as image edges and image segment boundaries are used to guide the basic signal-level fusion process. fusion of processed, higher level information to form a blueprint for fusion at signal level and fusion of information from multiple levels of extraction into a single fusion engine are both considered. When tested on two conventional signal-level fusion methodologies, such multi-level fusion structures eliminated undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness. Images produced by inclusion of multi-level information in the fusion process are clearer and of generally better quality than those obtained through conventional low-level fusion. This is verified through subjective evaluatio

关键词： image fusion signal-level fusion feature-level fusion multi-level fusion

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Simulation-based development of embedded sensor fusion applications

Simulation-based development of embedded sensor fusion appli...

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2nd IEEE International conference on Computational Cybernetics (ICCC 2004)

作者： Elmenreich, W Schlager, M Vienna Tech Univ A-1040 Vienna Austria

ISBN: (纸本)0780385888

This paper proposes a methodology for the design, testing, and fine-tuning of sensor fusion applications for distributed embedded sensor networks. The presented approach involves the following steps: (i) gathering data about the process environment, (ii) modeling of the process and the sensors to be used, (iii) selection of sensor fusion functions to process the sensor measurements, (iv) simulated closed-loop hardware-in-the-loop testing, (v) validation by open-loop testing in the real process, and, finally, (vi) application of the developed embedded sensor fusion application in the system. Particular steps may be repeated in a loop, if the result of a given step is not satisfactory. The main contribution of the approach is a separation of the modeling of process environment, sensors, and sensor processing. This allows for a strategic refinement of the model and supports the reuse and change of parts, e. g., in case of a sensor upgrade. The application of the approach is shown by a MATLAB/Simulink model that incorporates block diagrams describing process environment, sensor behavior, and sensor fusion algorithms.

关键词： sensor data fusion

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Multi-spectral image fusion for visual display

Multi-spectral image fusion for visual display

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

作者： Peli, T Peli, E Ellis, K Stahl, R Atlantic Aerosp Elect Corp Waltham MA 02154 USA

ISBN: (纸本)0819431931

This paper describes a contrast-based monochromatic fusion process. The fusion process is aimed for on board real time application and it is based on practical and computationally efficient image processing components. The process maximizes the information content in the combined image, while retaining visual clues that are essential for navigation/piloting tasks. The method is a multi scale fusion process that provides a combination of pixel selection from a single image and a weighing of the two/multiple images. The spectral region is divided into spatial sub bands of different scales and orientations, and within each scale a combination rule for the corresponding pixels taken from the two components is applied. Even when the combination rule is a binary selection the combined fused image may have a combination of pixel values taken from the two components at various scales since it is taken at each scale. The visual band input is given preference in low scale, large features fusion. This fusion process provides a fused image better tuned to the natural and intuitive human perception. This is necessary for pilotage and navigation under stressful conditions, while maintaining or enhancing the targeting detection and recognition performance of proven display fusion methodologies. The fusion concept was demonstrated against imagery from image intensifiers and forward looking infrared sensors currently used by the U.S. Navy for navigation and targeting. The approach is easily extendible to more than two bands.

关键词： image fusion contrast visual model night pilotage

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Optimal sensor dimension reduction in estimation fusion

Optimal sensor dimension reduction in estimation fusion

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conference on Multisensor, Multisource Information fusion

作者： Song, EB Zhu, YM Zhou, J Sichuan Univ Dept Math Chengdu 610064 Peoples R China

ISBN: (纸本)0819457981

When there exists the limitation of communication bandwidth between sensors and a fusion center, one needs to optimally pre-compress sensor outputs-sensor observations or estimates before sensors' transmission to obtain a constrained optimal estimation at the fusion center in terms of the linear minimum error variance criterion. This paper will give an analytic solution of the optimal linear dimensionality compression matrix for the single sensor case and analyze the existence of the optimal linear dimensionality compression matrix for the multisensor case, as well as how to implement a Gauss-Seidel algorithm to search for an optimal solution to linear dimensionality compression matrix.

关键词： multisensor estimation fusion linear compression minimum variance estimation

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