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

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Soft adaptive fusion of sensor energy for large-scale sensor networks (SAFE)

Soft adaptive fusion of sensor energy for large-scale sensor...

引用

Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2009

作者： Rababaah, Haroun Shirkhodaie, Amir Center of Excellence for Battlefield Sensor Fusion Department of Mechanical and Manufacturing Engineering Tennessee State University 3500 John Merritt Blvd. Nashville TN 37209 United States

ISBN: (纸本)9780819476111

Target tracking for network surveillance systems has gained significant interest especially in sensitive areas such as homeland security, battlefield intelligence, and facility surveillance. Most of the current sensor network protocols do not address the need for multi-sensor fusion-based target tracking schemes, which is crucial for the longevity of the sensor network. In this paper, we present an efficient fusion model for target tracking in a cluster-based large sensor networks. This new scheme is inspired by the image processing techniques by perceiving a sensor network as an energy map of sensor stimuli and applying typical image processing techniques on this map such as: filtering, convolution, clustering, segmentation, etc to achieve high-level perceptions and understanding of the situation. The new fusion model is called Soft Adaptive fusion of sensor Energies (SAFE). SAFE performs soft fusion of the energies collected by a local region of sensors in a large-scale sensor network. This local fusion is then transmitted by the head node to a base-station to update the common operation picture with evolving events of interest. Simulated scenarios showed that SAFE is promising by demonstrating a significant improvement in target tracking reliability, uncertainty, and efficiency. © 2009 SPIE.

关键词： sensor networks

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An unified approach to the fusion of imperfect data?

An unified approach to the fusion of imperfect data?

引用

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

作者： Florea, MC Jousselme, AL Grenier, D Bossé, É Univ Laval Dept Genie Elect LRTS Ste Foy PQ G1K 7P4 Canada

ISBN: (纸本)0819444812

For several years, researchers have explored the unification of the theories enabling the fusion of imperfect data and have finally considered two frameworks: the random sets and the conditional events algebra. Traditionally, the information is modeled and fused in one of the known theories: bayesian, fuzzy sets, possibilistic, evidential, or rough sets... Previous work has shown what kind of imperfect data these theories can best deal with. So, depending on the quality of the available information (uncertain, vague, imprecise,...), one particular theory seems to be the preferred choice for fusion. However, in a typical application, the variety of sources provides different kinds of imperfect data. The classical approach is then to model and fuse the incoming data in a single theory being previously chosen. In this paper, we first introduce the various kinds of imperfect data and then the theories that can be used to cope with the imperfection. We also present the existing relationships between them and detail the most important properties for each theory. We finally propose the random sets theory as a possible framework for unification, and thus show how the individual theories can fit in this framework.

关键词： imperfect data data fusion random sets

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Interferometric side scan sonar and data fusion

Interferometric side scan sonar and data fusion

引用

Conference on sensor fusion: architectures, algorithms, and applications IV

作者： Sintes, C Solaiman, B GESMA F-29240 Brest France

ISBN: (纸本)0819436771

This paper concerns the possibilities of sea bottom imaging and altitude determining of each imaged point. The performances of new side scan sonars which are able to image the sea bottom with a high definition and are able to evaluate the relief with the same definition derived from an interferometric multisensor system. The drawbacks concern the precision of the numerical altitude model. One way to improve the measurements precision is to merge all the information issued from the multi-sensors system. This leads to increase the Signal to Noise Ratio (SNR) and the robustness of the used method. The aim of this paper is to clearly demonstrate the ability to derive benefits of all information issued from the three arrays side scan sonar by merging : a. the three phase signals obtained at the output of the sensors, b. this same set of data after the application of different processing methods, and c, the a priori relief contextual information. The key idea the proposed fusion technique is to exploit the strength and the weaknesses of each data element in the fusion of process so that the global SNR will be improved as well as the robustness to hostile noisy environments.

关键词： Interferometry Image fusion sensors Electronic filtering Data fusion Image filtering Signal to noise ratio Gaussian filters Signal processing Wavefronts

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fusion rule estimation using vector space methods

Fusion rule estimation using vector space methods

引用

Conference on sensor fusion - architectures, algorithms, and applications

作者： Rao, NSV Oak Ridge National Lab. (United States)

ISBN: (纸本)081942482X

In a system of N sensors, the sensor S-j, j = 1,2...,N, outputs Y-(j) is an element of R, according to an unknown probability distribution P-Y(j/X);corresponding to input X is an element of [0.1]. A training n-sample (X-1, X-1), (X-2, Y-2), ...((X)n, Y-n) is given where Y-i = (Y-i((1)), Y-i((2)),...,Y-i((N))) such that Y-i((j)) is the output of S-j in response to input X-i. The problem is to estimate a fusion rule f: R-N proves [0.1], based on the sample, such that the expected square error is minimized over a family of function F that constitute a vector space. The function f* that minimizes the expected error cannot be computed since the underlying densities are unknown, and only an approximation (f) over cap to f* is feasible. We estimate the sample size sufficient to ensure that f provides a close approximation to f* with a high probability. The advantage of vector space methods are two-fold: (a) the sample size estimate is a simple function of the dimensionality of f and (b) the estimate (f) over cap can be easily computed by well-known least square methods in polynomial time. The results are applicable to the classical potential function methods and also (to a recently proposed) special class of sigmoidal feedforward neural networks.

关键词： sensor fusion fusion rule estimation empirical estimation vector space methods

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Performance limits for multi-platform scene-referenced navigation systems.

Performance limits for multi-platform scene-referenced navig...

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

作者： Ralph, JF Januarius, EM Smith, MI Edwards, KL Bernhardt, M Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)0819440809

The accuracy of aircraft/weapon navigation systems has improved dramatically since the introduction of global positioning systems and terrain-referenced navigation systems into integrated navigation suites. Future improvements, in terms of reliability and accuracy, could arise from the inclusion of navigation systems based on the correlation of known ground features with imagery from a visual band or infrared sensor, often called scene matching and area correlation or scene-referenced navigation. This paper considers the use of multi-platform fusion techniques to improve on the performance of individual scene-referenced navigation systems. Consideration is also given to the potential benefits of multi-platform fusion for scene-referenced object localisation algorithms that could be used in association with infrared targeting aids.

关键词： visual navigation sensor fusion distributed sensor systems object localization

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Real time sensor validation and fusion for distributed autonomous sensors

Real time sensor validation and fusion for distributed auton...

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Conference on Multisensor, Multisource Information fusion

作者： Yuan, XJ Li, XS Buckles, B Tulane Univ EECS Dept New Orleans LA 70118 USA

ISBN: (纸本)0819453579

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

关键词： real time sensor validation and fusion distributed sensor system design dynamic validation curve file based knowledge system confidence weighted averaging robust diagnostic maps

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Proceedings of SPIE: Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2005

Proceedings of SPIE: Multisensor, Multisource Information Fu...

引用

Multisensor, Multisource Information fusion: Architecture, algorithms, and applications 2005

The proceedings contain 35 papers from the conference on Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2005. Topics discussed include: reduced data projection slice image fusion using principal component analysis;real-time implementation of image alignment and fusion;image fusion with the hybrid evolutionary algorithm and response analysis;a perceptual quality metric for image fusion based on regional information;multispectral sensor fusion using the projection-slice theorem;a situation awareness model applied to multiple domains;hybrid framework for fusion 2+ in multi-multi air engagement;and product-based multiresolution image fusion.

关键词： sensor data fusion

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Robust speech separation using visually constructed speech signals

Robust speech separation using visually constructed speech s...

引用

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

作者： Aarabi, P Khameneh, NH Univ Toronto Dept Elect & Comp Engn Toronto ON M5M 3G4 Canada

ISBN: (纸本)0819444812

A technique to virtually recreate speech signals entirely from the visual lip motions of a speaker is proposed. By using six geometric parameters of the lips obtained from the Tulips1 database, a virtual speech signal is recreated by using a 3.6s audiovisual training segment as a basis for the recreation. It is shown that the virtual speech signal has an envelope that is directly related to the envelope of the original acoustic signal. This visual signal envelope reconstruction is then used as a basis for robust speech separation where all the visual parameters of the different speakers are available. It is shown that, unlike previous signal separation techniques, which required an ideal mixture of independent signals, the mixture coefficients can be very accurately estimated using the proposed technique in even non-ideal situations.

关键词： audiovisual information fusion blind speech separation independent component analysis audiovisual signal separation robust speech recognition

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A Copula Based Semi-parametric Approach for Footstep Detection Using Seismic sensor Networks

A Copula Based Semi-parametric Approach for Footstep Detecti...

引用

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

作者： Sundaresan, Ashok Subramanian, Arun Varshney, Pramod K. Damarla, Thyagaraju Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Army Res Labs Adelphi MD 20783 USA

ISBN: (纸本)9780819481740

In this paper, we consider the problem of detecting the presence of footsteps using signal measurements from a network of seismic sensors. Since the sensors are closely spaced, they result in correlated measurements. A novel method for detection that exploits the spatial dependence of sensor measurements using copula functions is proposed. An approach for selecting the copula function that is most suited for modeling the spatial dependence of sensor observations is also provided. The performance of the proposed approach is illustrated using real footstep signals collected using an experimental test-bed consisting of seismic sensors.

关键词： sensors Seismic sensors Data modeling sensor networks Statistical modeling Signal detection Performance modeling Analytical research Statistical analysis Surveillance

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Image restoration in multi-sensor missile seeker environments for design of intelligent integrated processing architectures

Image restoration in multi-sensor missile seeker environment...

引用

Conference on Image Reconstruction and Restoration ii

作者： Sundareshan, MK Pang, HY Amphay, S Sundstrom, B Univ. of Arizona (United States) Air Force Wright Lab. (United States)

ISBN: (纸本)0819425923

Two major factors that could limit successful implementations of image restoration and superresolution algorithms in missile seeker applications ate, (i) lack of accurate knowledge of sensor point spread function (PSF) parameters, and (ii) noise-induced artifacts in the restoration process. The robustness properties of a recently developed blind iterative Maximum Likelihood (ML) restoration algorithm to inaccuracies in sensor PSF are established in this paper. Two modifications to this algorithm that successfully equip it to suppress artifacts resulting from the presence of high frequency noise components are outlined. Performance evaluation studies with one-dimensional and two-dimensional signals are included to demonstrate that these algorithms have superresolution capabilities while possessing also attractive robustness and artifact suppression properties. The algorithms developed here hence contribute to efficient designs of intelligent integrated processing architectures for smart weapon applications.

关键词： Image processing Algorithm development Image restoration Missiles Point spread functions sensors Super resolution Weapons

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