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conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2010

作者： Shirkhodaie, Amir Rababaah, Haroun Tennessee State Univ Dept Mech & Mfg Engn Ctr Excellence Battlefield Sensor Fus Nashville TN 37209 USA

ISBN: (纸本)9780819481740

This paper presents a Multi-Layered Context Impact Modulation (MCIM) technique for persistent surveillance systems (PSS) and discusses its layered architecture for different context modulations including: spatial, temporal, sensor reliability, human presence, and environmental modulations. This paper also presents a fusion model for enhancement of focus of attention at the common operation picture (COP). The fusion model combines all the impacts from the different MCIM layers onto one unified modulated map. To test and evaluate the performance of MCIM, several experiments were conducted to modulate interaction of humans and vehicles which exhibit various normal and suspicious behaviors. The experimental results show strength of this approach in correctly modulating different suspicious situations with higher degree of certainty.

关键词： Persistent Surveillance Systems Situation Assessment Context Impact Layered Modulation Energy Logic fusion Engine Large-scale sensor Networks Soft fusion

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Data level fusion using common symmetry set

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 327-331页

作者： Tari, Sibel Middle East Technical Univ Ankara Turkey

Availability of different imaging modalities requires techniques to process and combine information from different images of the same phenomena. We present a symmetry based approach for combining information from multiple images. fusion is performed at data level. Actual object boundaries and shape descriptors are recovered directly from raw sensor output(s). Method is applicable to arbitrary number of images in arbitrary dimension.

关键词： sensor data fusion

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Combination of fuzzy sets and Dempster-Shafer theories in forest map updating using multispectral data

Combination of fuzzy sets and Dempster-Shafer theories in fo...

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

作者： Leduc, F Solaiman, B Cavayas, F ENST Bretagne Dept ITI F-29285 Brest France

ISBN: (纸本)0819440809

This paper explains a new approach to change detection and interpretation in a context of forest map updating. In this temporal change analysis we use a data set composed of map at time To and a satellite image at time T-1 and we refer to this as a mixed fusion approach. The analysis of remotely sensed data always necessitates the use of approximate reasoning. For this purpose, we use fuzzy logic to evaluate the objects' membership values to the considered classes and the Dempster-Shafer theory to analyse the confusion between classes and to find the more evident class to which an object belong.

关键词： map updating forestry change detection fuzzy logic Dempster-Shafer theory data fusion

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Information fusion for onboard and offboard avionics

Information fusion for onboard and offboard avionics

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

作者： Kurien, T Chao, A Gully, SW Wood, A ALPHATECH Inc Burlington MA 01803 USA

ISBN: (纸本)0819428256

This paper defines and demonstrates an all-source information fusion system for combining onboard and offboard data and maintain continuous track on targets. We provide an architecture containing an offboard data processor to extract data relevant to the attack aircraft mission, and a set of fusion modules for recursively associating sensor reports, tracking targets, and classifying targets. These modules are derived from a well-posed mathematical formulation which enables us to define precise interfaces among the fusion modules. This approach provides three benefits. First, it enables us to construct a fusion algorithm with close-to-optimal target tracking and classification performance. Second, it allows us to study new fusion algorithms by implementing alternate algorithms for each module. Third, it allows us to process data from any combination of sensors making the architecture applicable to a variety of attack aircraft and missions. We show that the proposed system can increase a pilot's situational awareness by providing him with a clearer battlefield picture consistent with attack aircraft mission objectives. Results for a simple but realistic air-to-ground scenario simulation demonstrate the benefits of fusing data from offboard and onboard sensors.

关键词： all source fusion Multiple Hypothesis Tracking (MHT) image gee-registration continuous tracking onboard-offboard fusion Kalman filtering

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Intelligent fusion processing in BMD applications

Intelligent fusion processing in BMD applications

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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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The system parameter fusion principle and its application to fuzzy pattern recognition

The system parameter fusion principle and its application to...

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

作者： Pu, QG Univ Sci & Technol Ctr Comp Suzhou 215011 Jiangsu Peoples R China

ISBN: (纸本)0819449598

Modern technology provides a great amount of information. In computer monitoring systems or computer control systems, especially real-time expert systems, in order to have the situation in hand, we need one or two parameters to express the quality and/or security of the whole system. This paper presents a principle for synthesizing measurements of multiple system parameters into a single parameter and its application to fuzzy pattern recognition.

关键词： sensor fusion fuzzy pattern recognition computer monitoring systems computer control systems realtime expert systems

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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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A Metamaterial-Inspired Combined Inductive-Capacitive sensor

A Metamaterial-Inspired Combined Inductive-Capacitive Sensor

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

作者： Long, Jiang Wang, Bingnan Mitsubishi Elect Res Labs Cambridge MA 02139 USA

ISBN: (纸本)9781628410587

This paper reports a metamaterial inspired combined inductive-capacitive sensing method for detecting and distinguishing metallic and non-metallic objects. Metallic and non-metallic objects can be distinguished by measuring both of their inductive and capacitive responses based on the fact that they respond differently to inductive and capacitive sensing. The proposed method is inspired by metamaterial structures. Both inductive and capacitive sensing are simultaneously realized when the sensor is operating at off-resonant frequencies. The proposed method is demonstrated with typical printed circuit board (PCB) technology. The designed sensor can distinguish the metallic and dielectric objects with a sensing range about 10 mm, showing a competitive performance compared with commercially available proximity sensors.

关键词： proximity sensor metamaterials combined sensing

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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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sensor management in an ASW data fusion system

Sensor management in an ASW data fusion system

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

作者： Penny, DE Def Evaluat & Res Agcy Malvern WR14 3PS Worcs England

ISBN: (纸本)0819431931

The Multi-sensor fusion Management (MSFM) algorithm positions multiple, detection-only, passive sensors in a two-dimensional plane to optimise the fused probability of detection using a simple decision fusion method, previously the MSFM algorithm was evaluated on two synthetic problem domains comprising of both static and moving targets(1). In the original formulation the probability distribution of the target location was modelled using a non-parametric approach. The logarithm of the fused detection probability was used as a criterion function for the optimisation of the sensor positions. This optimisation used a straightforward gradient ascent approach, which occasionally found local optima. Following the placement optimisation the sensors were deployed and the individual sensor detections combined using a logical OR fusion rule. The target location distribution could then be updated using the method of sampling, importance re-sampling (SIR). In the current work the algorithm is extended to admit a richer variety of behaviour. More realistic sensor characteristic models are used which include detection-plus-bearing sensors and false alarm probabilities commensurate with actual sonar sensor systems. In this paper the performance of the updated MSFM algorithm is illustrated on a realistic anti-submarine warfare (ASW) application(2) in which the placement of the sensors is carried out incrementally, allowing for the optimisation of both the location and the number of sensors to be deployed.

关键词： sensor fusion resource allocation anti-submarine warfare (ASW)

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