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

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A fuzzy model-based multi-sensor data fusion system

A fuzzy model-based multi-sensor data fusion system

引用

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

作者： Prajitno, P Mort, N Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)0819440809

A fuzzy model-based multi-sensor data fusion system is presented in this paper. The system is capable of accommodating both non-linear sensors of the same type and different (non-commensurate) sensors and to give accurate information about the observed system state by combining readings from them at feature/decision level. The data fusion system consists of process model and knowledge-based sensor model units based on a fuzzy inference system that predicts the future system and sensor states based on the previous states and the inputs. The predicted state is used as a reference datum in the sensor validation process which is conducted through a fuzzy classifier to categorise each sensor reading as a valid or invalid datum. The data fusion unit combines the valid sensor data to generate the feature/decision output. The corrector unit functions as a filtering unit to provide the final decision on the value of the current state based on the current measurement (fused output) and the predicted state. The results of the simulation of this system and other data fusion systems have been compared to justify the capability of the system.

关键词： multisensor data fusion fuzzy inference system function approximation predictive-based data fusion method

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Leveraging Provenance to Improve Data fusion in sensor Networks

Leveraging Provenance to Improve Data Fusion in Sensor Netwo...

引用

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

作者： Dogan, Gulustan Seo, Eunsoo Brown, Theodore Abdelzaher, Tarek F. CUNY New York NY 10021 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780819490858

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations.

关键词： Provenance Data fusion sensor Networks

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Decision fusion strategies for target detection with a three-sensor suite

Decision fusion strategies for target detection with a three...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Dasarathy, BV Dynetics Inc. (United States)

ISBN: (纸本)081942482X

Boolean logic based decision fusion strategies for target detection with two sensors have been studied in detail in the literature over the years. Increasing the number of sensors to three, offers an aided dimension of flexibility in the design of fusion strategies. One could visualize a single stage fusion wherein the decision outputs of all the three sensor subsystems are fused simultaneously under a variety of strategies such as AND logic, or majority (simple or firm decisions only) logic, or a no-firm contradiction logic. Alternatively, one could explore a two-stage fusion strategy, wherein either an AND or an OR logic is used at the first stage combining the decisions of two of the sensor subsystems, followed by a similar logic choice combining the fused decision from the first level with the decision from the third sensor subsystem. Of these strategies, while some may turn out to be equivalent in a mathematical sense, others remain clearly unique. The study analyzes these strategies to assess their relative benefits. The papers concludes with a brief discussion on possible extensions in terms of temporal fusion strategies that exploit information derived from multiple looks and the potential for application to real-world problems such as mine detection.

关键词： sensor fusion decision fusion fusion strategies three-sensors suite mine detection

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Intelligent fusion processing in BMD applications

Intelligent fusion processing in BMD applications

引用

Conference on sensor fusion - architectures, algorithms, and applications

作者： McCullough, C Byrd, K Bjork, C Grider, G Smith, B Morris, N Dasarathy, B U.S. Army Space and Strategic Defense Command (United States) Nichols Research Corp. (United States) Dynetics Inc. (United States)

ISBN: (纸本)081942482X

Intelligent processing techniques are applied to a Ballistic Missile Defense (BMD) application, focused on classifying the objects in a typical threat complex, using fused IR and Ladar sensors. These techniques indicate the potential to improve designation robustness against ''off-normal''/unexpected conditions, or when sensor data or classifier performance degrades. A Fused sensor Discrimination (FuSeD) simulation testbed was assembled for designation experiments, to evaluate test and simulation data, assess intelligent processor and classification algorithms, and evaluate sensor performance. Results were produced for a variety of neural net and other nonlinear classifiers, yielding high designation performance and low false alarm rates. Most classifiers yield a few percent in false alarm rate: rates are further improved when multiple techniques are applied via a Majority Based fusion technique. Example signatures, features, classifier descriptions, intelligent controller design, and architecture ate included. Work was performed for the Discriminating Interceptor Technology Program (DITP).

关键词： sensors LIDAR Intelligent sensors Neural networks Data fusion Doppler effect Computer simulations Detection and tracking algorithms Infrared sensors Infrared signatures

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Integrated Multi-sensor fusion for Mapping and Localization in Outdoor Environments for Mobile Robots

Integrated Multi-sensor Fusion for Mapping and Localization ...

引用

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

作者： Emter, Thomas Petereit, Janko Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany

ISBN: (纸本)9781628410587

An integrated multi-sensor fusion framework for localization and mapping for autonomous navigation in unstructured outdoor environments based on extended Kalman filters (EKF) is presented. The sensors for localization include an inertial measurement unit, a GPS, a fiber optic gyroscope, and wheel odometry. Additionally a 3D LIDAR is used for simultaneous localization and mapping (SLAM). A 3D map is built while concurrently a localization in a so far established 2D map is estimated with the current scan of the LIDAR. Despite of longer run-time of the SLAM algorithm compared to the EKF update, a high update rate is still guaranteed by sophisticatedly joining and synchronizing two parallel localization estimators.

关键词： multi-sensor fusion extended Kalman filter particle filter SLAM autonomous mobile robot asynchronous fusion 3D LIDAR mapping

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Practical Considerations in Bayesian fusion of Point sensors

Practical Considerations in Bayesian Fusion of Point Sensors

引用

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

作者： Johnson, Kevin Minor, Christian USN Res Lab Washington DC 20375 USA Nova Res Inc Alexandria VA 22308 USA

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented.

关键词： sensor fusion Bayes decision theory

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A trainable decisions in-decision out (DEI-DEO) fusion system

A trainable decisions in-decision out (DEI-DEO) fusion syste...

引用

sensor fusion: architectures, algorithms and applications ii

作者： Dasarathy, Belur V. Dynetics Inc. P. O. Box 5500 Huntsville AL 35814-5500 United States

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i. e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

关键词： sensor data fusion

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

引用

20th IEEE Conference on Signal Processing - algorithms, architectures, Arrangements, and applications (SPA)

作者： Kroeger, M. Sauer-Greff, W. Urbansky, R. Lorang, M. Siegrist, M. Univ Kaiserslautern Chair Commun Engeneering Kaiserslautern Germany WIPOTEC GmbH Kaiserslautern Germany

ISBN: (纸本)9788362065271

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transforms

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Feature level sensor fusion

Feature level sensor fusion

引用

Conference on sensor fusion - architectures, algorithms, and applications iiI

作者： Peli, T Young, M Knox, R Ellis, K Bennett, F Atlantic Aerosp Elect Corp Waltham MA 02154 USA

ISBN: (纸本)0819431931

This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor, However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways (each sensor as primary) and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data and were shown to provide substantial False alarm reduction. The approaches are easily extendable to more than two sensors.

关键词： sensors Image fusion Data fusion Synthetic aperture radar Target detection Feature extraction sensor fusion Image processing Infrared sensors Target recognition

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2012

Multisensor, Multisource Information Fusion: Architectures, ...

引用

Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2012

ISBN: (纸本)9780819490858

This proceedings contains 27 papers. These conferences provide a general discussion and dissemination for researchers and designers of recent advances in field of Multisensor, Multisource Information fusion: architectures, algorithms, and applications. Papers published were categorized under seven sessions, i.e. Session 1: Information fusion Approaches and algorithms with 4 papers;Session 2: Information fusion Approaches and algorithms ii with 3 papers;Session 3: Information fusion in Robotics with 6 papers;Session 4: Information fusion Approaches and algorithms iiI (biometrics-related) with 3 papers;Session 5: Image fusion with 3 papers;Session 6: Information fusion Approaches and algorithms IV (human-in-the-loop) with 5 papers;Session 7: Information fusion Systems and Evaluation Measures with 2 papers. Besides, a paper was published as poster session. The key terms of the proceeding papers include sensor fusion;Intensity interferometry;Data Association;Intrusion protection;Network trace generators;Bayesian fusion;Layered sensing systems.

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