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

作者： Belur, Sv Dasarathy, Bv Comp Sci Corp Dept Appl Res Syst Sci Div Lanham MD 20706 USA

ISBN: (纸本)0819428256

The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization teal, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

关键词： feature level fusion feature optimization features in - feature out fusion feature synthesis

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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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Estimation of subpixel-resolution motion fields from segmented image sequences

Estimation of subpixel-resolution motion fields from segment...

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

作者： Schultz, RR Stevenson, RL Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819428256

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRvS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. However, estimating an accurate subpixel-resolution motion field between two low-resolution, noisy video frames has proven to be a formidable challenge. Up-sampling the image sequence frames followed by the application of block matching, optical flow estimation, or Bayesian motion estimation results in relatively poor subpixel-resolution motion fields, and consequently inaccurate regions within the super-resolution enhanced video still. This is particularly true for large interpolation factors (greater than or equal to 4). To improve the quality of the subpixel motion fields and the corresponding HRvS, motion can be estimated for each object within a segmented image sequence. First, a reference video frame is segmented into its constituent objects, and a mask is generated for each object which describes its spatial location. As described previously, subpixel-resolution motion estimation is then conducted by video frame up-sampling followed by the application of a motion estimation algorithm. Finally, the motion vectors are averaged over the region of each mask by applying an a-trimmed mean filter to the horizontal and vertical components separately. Since each object moves as a single entity, averaging eliminates many of the motion estimation errors and results in much more consistent subpixel motion fields. A substantial improvement is also visible within particular regions of the HRvS estimates. Subpixel-resolution motion fields and HRvS estimates are computed for interpolation factors of 2, 4, 8, and 16, to examine the benefits of object segmentation and motion field averaging.

关键词： interpolation motion estimation segmentation sensor fusion super-resolution enhancement

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A non-ad-hoc decision rule for Dempster-Shafer method of evidential reasoning

A non-ad-hoc decision rule for Dempster-Shafer method of evi...

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sensor fusion: architectures, algorithms and applications II

作者： Cheaito, Ali Lecours, Michel Bossé, Éloi Electrical and Computer Eng. Dept. Universite Laval Laval Que. G1K 7P4 Canada

This paper is concerned with the fusion of identity information through the use of statistical analysis rooted in Dempster-Shafer theory of evidence to provide automatic identification aboard a platform. An identity information process for a baseline Multi-Source Data fusion (MSDF) system is defined. The MSDF system is applied to information sources which include a number of radars, IFF systems, an ESM system, and a remote track source. We use a comprehensive Platform Data Base (PDB) containing all the possible identity values that the potential target may take, and we use the fuzzy logic strategies which enable the fusion of subjective attribute information from sensor and the PDB to make the derivation of target identity more quickly, more precisely, and with statistically quantifiable measures of confidence. The conventional Dempster-Shafer lacks a formal basis upon which decision can be made in the face of ambiguity. We define a non-ad hoc decision rule based on the expected utility (EUI) interval for pruning the "unessential" propositions which would otherwise overload the real-time data fusion system. An example has been selected to demonstrate the implementation of our modified Dempster-Shafer method of evidential reasoning.

关键词： sensor data fusion

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Multisensor data fusion

GEC JOURNAL OF TECHNOLOGY

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GEC JOURNAL OF TECHNOLOGY 1998年第1期15卷 27-37页

作者： Grossmann, P GMRCB Borehamwood England

Growing demand for better sensing and monitoring systems and for more effective means of processing the increasing flow of information has led to the wider implementation of multisensor data fusion techniques. With the recent rapid developments in sensor technology and in the advanced methods of signal and data processing, multisensor data fusion is becoming one of the most important new techniques to be used in many military, industrial and commercial applications. This article offers a brief overview of the fusion area illustrated by several examples of established techniques, their limitations, proposed new solutions and other new developments.

关键词： multisensor multisource data fusion tracking classification situation assessment algorithms architectures

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Distributed fusion architectures and algorithms for target tracking

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PROCEEDINGS OF THE IEEE 1997年第1期85卷 95-107页

作者： Liggins, ME Chong, CY Kadar, I Alford, MG vannicola, v Thomopoulos, S BOOZ ALLEN & HAMILTON INC SUNNYVALECA 94089 GRUMMAN CORP ADV TECHNOL & DEV CTRBETHPAGENY 11714

Modem surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets, In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow is distributed fusion systems and for developing algorithms. fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.

关键词： Distribute ARCHITECTURE data association sensor Data algorithms fusion (melting) Target tracking fusion fusion reactions fusion, biological SURvEILLANCE SYSTEMS

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A linearly constrained least squares approach for multisensor data fusion

A linearly constrained least squares approach for multisenso...

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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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Multiple sensor fusion for long range surveillance: Design and performance issues

Multiple sensor fusion for long range surveillance: Design a...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Jones, G Raytheon Canada Ltd. (Canada)

ISBN: (纸本)081942482X

This paper provides a description and detailed review of a multiple hypothesis tracking system, which handles data from two radars, and a number of other sources. An efficient method for processing detections from two (time-offset) radars, and integrating them in the multiple hypothesis framework will be described. The implications of such a methodology on the tracking filter will also be discussed. The paper then explains the algorithm employed for fusion of Automatic Dependent Surveillance reports into the system, and concludes with a demonstration of some sample results.

关键词： multiple-hypothesis tracking sensor fusion

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Identity and attribute information fusion using evidential reasoning

Identity and attribute information fusion using evidential r...

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Bosse, E Simard, MA Defence Research Establishment Valcartier (Canada) Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology that can enhance his/her decision making process regarding the identity of detected objects. Two identity information fusion architectures are discussed here. The first is concerned with the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration. The second is concerned with the fusion of attribute information using a modified version of the Dempster-Shafer evidential combination algorithm.

关键词： sensors Information fusion Data fusion Data processing Radar Electronic support measures Telecommunications Signal processing Databases Probability theory

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Decision level data fusion using Bayesian inference

Decision level data fusion using Bayesian inference

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Conference on sensor fusion - architectures, algorithms, and applications

作者： Parish, J TRW Inc. (United States)

ISBN: (纸本)081942482X

This paper describes a blackboard system for integrating observations from multiple sensors. Multiple sensors report observations to the blackboard system. The blackboard system correlates the observations to a set of active models, and the models are both temporally limited and also probabilistic. The design is object-oriented to allow for extensions that accommodate new models and sensors. An example application to a grid of sensors is presented

关键词： Bayesian inference blackboard systems data fusion

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