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conference on sensor fusion - architectures, algorithms, and applications

作者： Williams, A Pachowicz, P Ronk, L SAIC Arlington VA 22203 4001 N. Fairfax Dr. United States George Mason University Systems Engineering Dept. Fairfax VA 22030 4400 University Dr. United States

ISBN: (纸本)081942482X

In this paper, we discuss a new fusion architecture, including some preliminary results on field data. The architecture consists of a new decision level fusion algorithm, the Piecewise Level fusion Algorithm (PLFA), integrated with a new expert system based user assistant that adjusts PLFA parameters to optimize for a user desired classification performance. This architecture is applicable for both multisensor and multilook fusion. The user specifies classification performance by inputting entries for a desired confusion matrix at the fusion center. The intelligent assistant suggests input alternatives to reach the performance goal based on previously supplied user inputs and on performance specifications of the individual sensors. If deadlock results, i.e., the goal is not attainable because of conflicting user inputs, the assistant Rill inform the user. As the user and assistant interact, the assistant calculates the parameters necessary to automatically adjust the PLFA for the required performance. These parameters and calculations are hidden from the user. That is, the architecture is designed so that user inputs are intuitive for an unskilled operator. The implementation of this adaptable fusion architecture is due to the relatively simple structure of the PLFA and the expert system heuristic rules. We briefly describe the PLFA structure and operation, illustrate some expert system rules, and discuss preliminary performance of the entire architecture, including a sample dialog between the user and the intelligent assistant. We conclude this paper with a discussion of future extensions to this architecture that include replacing human interactions with dynamic learning techniques.

关键词： multi-sensor fusion multi-look fusion expert assistant decision level fusion adaptive systems fusion architecture

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Edge detection through information fusion using fuzzy and evidential reasoning concepts

Edge detection through information fusion using fuzzy and ev...

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

作者： Solaiman, B Lecornu, L Roux, C ENST Bretagne Dpt ITI F-29285 Brest France

ISBN: (纸本)0819436771

The main topic of this study concerns edge detection using information fusion approaches. Edge detection methods are based on first and second order local operations followed by a thresholding and edge tracking techniques. In this study, an intermediate fuzzy-evidential conceptual level is introduced between the gray level and edge detection symbolic information level. From the image, evidences concerning edges and regions are extracted using fuzzy membership functions as well as contextual information. The proposed approach can be decomposed into two steps: 1) application of evidential reasoning approach in order to compute a basic masse function, 2) edge detection process based on the use of an iterative algorithm, exploiting the contextual information and a belief masse function. Masse function computation is based of the use of edge and region fuzzy membership functions of each pixel in the analyzed scene. The main interest of this step is to consider membership functions as being observed evidences instead of image gray level values. The key idea of the second step is to use all the information about regions, edges and contextual data in the edge extraction process. Obtained results are encouraging and the proposed methodology is shown to be robust to different noisy environments.

关键词： evidential reasoning fuzzy edge detection evidential fusion information fusion

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Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection

Implementation and testing of a sensor-netting algorithm for...

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

作者： Gruber, Thomas Grim, Larry Fauth, Ryan Tercha, Brian Powell, Chris Steinhardt, Kristin MESH Inc Oxford PA 19363 USA

ISBN: (纸本)9780819486387

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e. g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused /un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

关键词： sensor netting algorithm fusion tomography map uncertainty consistency

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Fuzzy logic approach for data association

Fuzzy logic approach for data association

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Proceedings of the 1999 sensor fusion: architectures, algorithms, and applications III

作者： Stubberud, Stephen C. Lugo, Kristi A. ORINCON Corp San Diego CA United States

A fuzzy logic based data association routine has been developed. The concept is based on very simple fuzzy logic implementation. The resulting technique is intended as an enhancement to current data association routines when added information such as sensor blockage and forbidden terrain knowledge can be incorporated into the system.

关键词： sensor data fusion

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sensor fusion options for ballistic missile defense interceptor applications

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3719卷 92-102页

作者： Bjork, Charles Morris, Norman Dasarathy, Belur V. Smith, Bart Allen, Doug Prestwood, William T. Nichols Research Corp Huntsville United States

Critical elements of future exoatmospheric interceptor systems are intelligent processing (IP) techniques which can effectively combine sensor data from disparate sensors. This paper summarizes the impact on discrimination performance of several feature and classifier fusion techniques, which can be used as part of the overall IP approach. These techniques are implemented either within the Fused sensor Discrimination (FuSeD) Testbed, or off-line as building blocks that can be modified to assess differing fusion approaches, classifiers and their impact on interceptor requirements. Several optional approaches for combining the data at the different levels, i.e, feature and classifier levels, are discussed in this paper and a comparison of performance results is shown. Approaches yielding promising results must still operate within the timeline and memory constraints on board the interceptor. A hybrid fusion approach is implemented at the feature level through the use of feature sets input to specific classifiers (currently two classifiers are employed). The output of the fusion process contains an estimate of the confidence in the data and the discrimination decisions. The confidence in the data and decisions can be used in real time to dynamically select different sensor feature data, classifiers, or to request additional sensor data on specific objects that have not been confidently identified as 'lethal' or 'non-lethal'. However, dynamic selection requires an understanding of the impact of various combinations of feature sets and classifier options. Accordingly, the paper presents the various tools for exploring these options and illustrates their usage with data sets generated to realistically simulate the world of Ballistic Missile Defense (BMD) interceptor applications.

关键词： sensor data fusion

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Optimal features in feature out (FEI-FEO) fusion for decisions in multisensor environments

Optimal features in feature out (FEI-FEO) fusion for decisio...

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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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Architecture of triple sensitive colour digital sensors with the integration of image fusion techniques

Architecture of triple sensitive colour digital sensors with...

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conference on Multisensor, Multisource Information fusion

作者： Zhang, Y Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB E3B 5A3 Canada

ISBN: (纸本)0819457981

High resolution and highly sensitive colour digital sensors are desired for many applications, including military and civilian missions. Due to the limitation of spectral bandwidth, the sensitivity of a digital colour sensor is usually three times lower than that of a digital panchromatic sensor with a spectral bandwidth of the entire visible range or a range from visible to near infrared. This paper introduces a conceptual architecture for producing a triple sensitive colour digital frame sensor. Automatic image fusion techniques are involved to integrate colour and panchromatic images to increase the sensitivity of the colour sensor. Available satellite colour and panchromatic images are tested to prove the concept. The test results demonstrate that the introduced architecture is promising for developing real triple sensitive colour digital sensors.

关键词： digital sensor colour high sensitivity high resolution image fusion

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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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Multi-sensor feature fusion methods and results

Multi-sensor feature fusion methods and results

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

作者： Heagy, D Barnes, R Bechhoefer, E Mitre Corp MS W967 Mclean VA 22101 USA

ISBN: (纸本)0819440809

Analysts responsible for supporting time dominated threat decisions are faced with a growing volume of sensor data. Most efforts to increase discrimination among targets using multiple types of sensors encounter the same problems: sensor data are received in large volumes. sensor data are highly variable. Signature features are represented by many dimensions. Feature values are intercorrelated, random or not related to target differences. Decision rules for classifying new target data are difficult to define. This paper describes a new methodology for solving several problems: selecting signature features, reducing variability, increasing discrimination accuracy, and developing decision rules for classifying new target signatures. The results from using a combination of exploratory and multivariate statistical techniques show potential improvements over the traditional Dempster-Shafer approach. This project uses data from operational prototype sensors and vehicles of interest for threat analysis. Acoustic and seismic sensor data came from an unattended ground sensor and three military vehicles. Although the resulting algorithms are specific to the data set, the data screening and fusion methods tested in this project may be useful with other types of sensor and target data.

关键词： feature fusion multisensor classification data screening signal processing pattern recognition feature templates multivariate statistics Dempster-Shafer threat analysis

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Distributed air-to-ground targeting

Distributed air-to-ground targeting

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

作者： Ralph, JF Smith, MI Bernhardt, M West, CE Angell, CR Sims, SW Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England

ISBN: (纸本)0819444812

This paper examines the requirement for accurate estimates of the statistical correlations between measurements in a distributed air-to-ground targeting system. The study uses results from a distributed multi-platform targeting simulation based on a level-1 data fusion system to assess the extent to which correlated measurements can degrade system performance, and the degree to which these effects need to be included to obtain a required level of accuracy. The data fusion environment described in the paper incorporates a range of target tracking and data association algorithms, including several variants of the standard Kalman filter, probabilistic association techniques and Reid's multiple hypothesis tracker. A variety of decentralized architectures are supported, allowing comparison with the performance of equivalent centralized systems. In the analysis, consideration is given to constraints on the computational complexities of the fusion system, and the availability of estimates of the measurement correlations and platform-dependent biases. Particular emphasis is placed on the localisation accuracy achieved by different algorithmic approaches and the robustness of the system to errors in the estimated covariance matrices.

关键词： targeting object localisation data fusion

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