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

共 580 条记录，以下是111-120 订阅

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A linearly constrained least squares approach for multisensor data fusion

A linearly constrained least squares approach for multisenso...

引用

Conference on sensor fusion - architectures, algorithms, and applications

作者： Zhou, YF Leung, H Telexis Corporation Canada Ottawa ON K1V 7P2 2427 Holly Lane Canada Surface Radar Section Defence Research Establishment Ottawa Ottawa ON K1A OK2 Canada

ISBN: (纸本)081942482X

In this paper, we present a linearly constrained least squares (LCLS) algorithm for multisensor data fusion. While fusion is considered in the scope of linear combination, the objective of the LCLS algorithm is to minimize the energy of the linearly fused information based on empirical sensory information. Statistical performance analysis of the LCLS algorithm will be carried out including the consistency and asymptotic covariance of the estimates. Effectiveness of the proposed fusion algorithm will be evaluated numerically based on fusion of signals and images.

关键词： data fusion least squares minimum variance information uncertainty multisensor tracking consistency analysis

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fusion under the closed-world assumption

Fusion under the closed-world assumption

引用

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

作者： Grégoire, E Univ Sci & Tech Lille Flandres Artois CRIL F-62307 Lens France

ISBN: (纸本)0819440809

In this paper, new operators for fusing logical knowledge-bases (KBs) are proposed. They are defined in such a way that they can handle KBs that must be interpreted under forms of the closed-world assumption. Such assumptions implicitly augment the KBs with some additional information that could not be deduced using the standard logical deductive apparatus. More precisely, we extend previous recent works about the logical fusion of knowledge to handle such KBs. We focus on the model-theoretic definition of fusion operators to show their limits. In particular, the basic logical concept of model appears too coarse-grained. We solve this problem and propose new operators that cover a whole family of fusion approaches in the presence of variants of the closed-world assumption.

关键词： data fusion knowledge base fusion CWA nonmonotonic logics

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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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PERFORMANCE AND LIMITATIONS OF DISTRIBUTED INFERENCE AND INFORMATION fusion IN WIRELESS sensor NETWORK applications

PERFORMANCE AND LIMITATIONS OF DISTRIBUTED INFERENCE AND INF...

引用

作者： Sherry, Richard Ray PennState University Libraries

学位级别：Doctor of Philosophy

Recent advances in sensor design and miniaturization have provided the opportunity for the creation of large distributed wireless sensor networks. There has been significant progress in combining sensing, processing, data storage and communications capabilities, in network self organization, and in optimizing communication architectures. In contrast to most other network applications, wireless sensor networks face a number of special challenges and constraints resulting from 1) lack of hardwired connections (no external power sources, low communications bandwidths, higher communication error rates), 2) small physical size (small onboard energy supply, small antennas/acoustic transducers, small low energy sensors) and 3) elevated sensor node failure rates. One of the key remaining challenges is in the area of inference and information fusion (aggregating/filtering/interpreting the sensor data into useful high level knowledge). Many authors have advocated the use of local distributed inference and fusion algorithms such as the iterative message-passing belief propagation algorithms employed on probabilistic graphical models (Bayesian Networks and Markov Random Fields). However, little research has been performed to assess the performance of these algorithms under the special constraints imposed by wireless sensor network applications. This dissertation reports on a study investigating issues associated with application of these algorithms to realistic wireless sensor networks configurations. This research has produced results delineating the performance and limitations including communications requirements, energy resource requirements and the impacts of different topologies and architectures such as hierarchical/non-hierarchical topologies, centralized or distributed processing, localized (in local node clusters) or full network models, and node cluster size.

关键词： wireless sensor networks information fusion data fusion

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Decision fusion in sensor Networks for Spectrum Sensing based on Likelihood Ratio Tests

Decision Fusion in Sensor Networks for Spectrum Sensing base...

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Conference on Advanced Signal Processing algorithms, architectures, and Implementantions XViiI

作者： Chung, Wei-Ho Yao, Kung Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

ISBN: (纸本)9780819472946

sensor networks have been shown to be useful in diverse applications. One of the important applications is the collaborative detection based on multiple sensors to increase the detection performance. To exploit the spectrum vacancies in cognitive radios, we consider the collaborative spectrum sensing by sensor networks in the likelihood ratio test (LRT) frameworks. In the LRT, the sensors make individual decisions. These individual decisions are then transmitted to the fusion center to make the final decision, which provides better detection accuracy than the individual sensor decisions. We provide the lowered-bounded probability of detection (LBPD) criterion as an alternative criterion to the conventional Neyman-Pearson (NP) criterion. In the LBPD criterion, the detector pursues the minimization of the probability of false alarm while maintaining the probability of detection above the pre-defined value. In cognitive radios, the LBPD criterion limits the probabilities of channel conflicts to the primary users. Under the NP and LBPD criteria, we provide explicit algorithms to solve the LRT fusion rules, the probability of false alarm, and the probability of detection for the fusion center. The fusion rules generated by the algorithms are optimal under the specified criteria. In the spectrum sensing, the fading channels influence the detection accuracies. We investigate the single-sensor detection and collaborative detections of multiple sensors under various fading channels, and derive testing statistics of the LRT with known fading statistics.

关键词： sensor networks cognitive radio lower-bounded probability of detection criterion Neyman-Pearson criterion decision fusion

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Feature level sensor fusion

Feature level sensor fusion

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

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

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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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Leveraging Provenance to Improve Data fusion in sensor Networks

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

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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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Multiple sources distributed information fusion based on multi-agent system

Multiple sources distributed information fusion based on mul...

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

作者： Xia, ZX E China Shipbldg Inst Dept Comp Sci Zhenjiang 212003 Peoples R China

ISBN: (纸本)0819444812

In a multi-node distributed decision system under some conditions there are few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. However if we treat this distributed system as a multi-agent system, and each node acts as an agent, it has some other node's historical experiences or knowledge for resolving problems and stored in additional case bases, so it can uses case based reasoning (CBR) and transposition reasoning to obtain the possible viewpoints or decisions of other nodes and then makes information fusion by itself This approach may reduce the subjectivism which is the weakness of pure transposition reasoning.

关键词： distributed decision system multi-agent case based reasoning transposition reasoning

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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...

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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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