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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012"

共 282 条记录，以下是271-280 订阅

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Investigation of automated feature extraction using multiple data sources

Investigation of automated feature extraction using multiple...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Harvey, NR Perkins, SJ Pope, PA Theiler, J David, NA Porter, RB Los Alamos Natl Lab Space & Remote Sensing Sci Grp Los Alamos NM 87545 USA

ISBN: (纸本)0819449598

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use. of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

关键词： supervised classification data fusion support vector machines feature extraction machine learning multispectral

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Level 5: User refinement to aid the fusion process

Level 5: User refinement to aid the fusion process

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Blasch, EP Plano, S Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)0819449598

The revised JDL fusion model Level 4 "process refinement" covers a broad spectrum of actions such as sensor management and control. A limitation of Level 4 is the purpose of control - whether it be for user needs or system operation. Level 5, User Refinement, is a modification to the Revised JDL model that distinguishes between machine process refinement and user refinement. User refinement can either be human control actions or refinement of the user's cognitive model. In many cases, fusion research concentrates on the machine and does not take full advantage of the human as not only a qualified expert to refine the fusion process, but also as customer for whom the fusion system is designed. Without user refinement, sensor fusion is incomplete, inadequate, and the user neglects its worthiness. To capture user capabilities, we. explore the concept of user refinement through decision and action based on situational leadership models. We develop a Fuse-Act Situational User Refinement (FASUR) model that details four refinement behaviors: Neglect, Consult, Rely, and Interact and five refinement functions: Planning, Organizing, Coordinating, Directing, and Controlling. Process refinement varies for different systems and different user information needs. By designing a fusion system with a specific user in mind, vis 'Level 5', a fusion architecture can meet user's information needs for varying situations, extend user sensing capabilities for action, and increase the human-machine interaction.

关键词： Sensors Data modeling Data fusion Sensor fusion Cognitive modeling Control systems Telecommunications Systems modeling Environmental sensing Surveillance

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Wireless sensors information fusion for structural health monitoring

Wireless sensors information fusion for structural health mo...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Ou, JP Li, HW Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China

ISBN: (纸本)0819449598

The process of implementing a damage detection strategy for engineering systems is often referred to as structural health monitoring (SHM). And Structural Health Monitoring is very important for large structures like suspension- and cable-stayed bridges, towers, offshore platforms and so on. Some advance technologies for infrastructure health monitoring have been caused much more attentions, in which the wireless sensor network (WSN) is recently received special interests. The WSN would have lower capital and installation costs as well as ensure more reliability in the communication of sensor measurements. However, in the context of untethered nodes, the finite energy budget is a primary design constraint. Therefore, one wants to process data as much as possible inside the network to reduce the number of bits transmitted, particularly over longer distances. In this, paper, a WSN is proposed for health monitoring of the offshore platform, and a laboratory prototype was designed and developed to demonstrate the feasibility and validity of the proposed WSN. In the laboratory prototype, wireless sensor nodes were deployed on a model of offshore platform, a Wireless Sensor Local Area Network (WSLAN) transfers the simulated data among personal computer and microsensor nodes peripherals without cables. To minimize the energy consumption, algorithms for fusing the acceleration, temp and magnetic sensors on a single node are being developed. And based on fusing the data from local nodes, the current state of structure health was determined. In our deployment, we using UC Berkeley motes as the wireless sensor nodes to examine many of the issues relating to their usage and our information fusion algorithm.

关键词： Structural Health Monitoring (SHM) wireless sensors wreless sensor networks information fusion

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Probabilistic and fuzzy information fusion applied to radar system ranking

Probabilistic and fuzzy information fusion applied to radar ...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Lecornu, L Debon, R Komorniczak, W Solaiman, B ENST Bretagne ITI Dpt F-29285 Brest France

ISBN: (纸本)0819449598

The decision making systems make use of heterogeneous information to identify an object class or a target, which are affected by various kinds of imperfection. First, information issued from measures (radar measures, images) of an observation is represented by X variables. Generally, on these X variables, each class can be described through a probability distribution function. These decision systems also integrate expert prior knowledge to assist the decision. Such information is defined by Y variables and is represented by fuzzy membership functions. The question is how to combine appropriately these two kinds of variables in order to improve the efficiency of the decision process. In this paper, we present a decision model combining probabilistic and fuzzy variables. The decision is defined using a fuzzy Bayesian approach, which takes into account these two imperfections. Only two classes are considered using one X variable and one Y variable. Then an extension is proposed to suit more complicated cases. To validate the interest of this approach, we compare it with the standard Bayesian classification and fuzzy classification, applied separately to synthetic data. In addition, we will see how our approach can be applied to the problem of radar system ranking, on which system resources are limited and as a consequence, decisions about priorities must be taken. Using the system information sources (i.e. probabilistic: radar measurements, fuzzy: prior expert knowledge, evidential), a comparison between Bayesian classification, fuzzy classification, system decision and the proposed approach is presented.

关键词： multifunction radar tracking fusion fuzzy Bayesian approach fuzzy classification system decision theoretical framework fusion

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Combining belief functions and fuzzy membership functions

Combining belief functions and fuzzy membership functions

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Florea, MC Jousselme, AL Grenier, D Bossé, T Univ Laval Quebec City PQ G1K 7P4 Canada

ISBN: (纸本)0819449598

In several practical applications of data fusion and more precisely in object identification problems, we need to combine imperfect information coming from different sources (sensors, humans, etc.), the resulting uncertainty being naturally of different kinds. In particular, one information could naturally been expressed by a membership function while the other could best be represented by a belief function. Usually, information modeled in the fuzzy sets formalism (by a membership function) concerns attributes like speed, length, or Radar Cross Section whose domains of definition are continuous. However, the object identification problem refers to a discrete and finite framework (the number of objects in the data base is finite and known). This implies thus a natural but unavoidable change of domain. To be able to respect the intrinsic characteristic of uncertainty arising from the different sources and fuse it in order to identify an object among a list of possible ones in the data base, we need (1) to use a unified framework where both fuzzy sets and belief functions can be expressed, (2) to respect the natural discretization of the membership function through the change of domain (from attribute domain to frame of discernment). In this paper, we propose to represent both fuzzy sets and belief function by random sets. While the link between belief functions and random sets, is direct, transforming fuzzy sets into random sets involves the use of a-cuts for the construction of the focal elements. This transformation usually generates a large number of focal elements often unmanageable in a fusion process. We propose a way to reduce the number of focal elements based on some parameters like the desired number of focal elements, the acceptable distance from the approximated random set to the original discrete one, or the acceptable loss of information.

关键词： random sets fuzzy sets belief functions object identification information fusion

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Mutual-aided target tracking and identification

Mutual-aided target tracking and identification

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multisensor. multisource information fusion: architectures, algorithms, and applications 2003

作者： Yang, Chun Blasch, Erik Sigtem Technology Inc. 113 Clover Hill Lane Harleysville PA 19438-1693 United States AFRL/SNAR - Sensor ATR 2010 Fifth Street WPAFB OH 45433-7001 United States

Mutual-aided target tracking and target identification schemes are described by exploiting the couplings between the target tracking and target identification systems, which are typically implemented in a separate manner. A hybrid state space approach is formulated to deal with continuous-valued kinematics, discrete-valued target type, and discrete-valued target pose (inherently continuous but quantized). We identify and analyze ten possible mutual aiding mechanisms with different complexity in different levels. The coupled tracker design is illustrated within the context of JointSTARS using GMTI and HRRR measurements as well as digital terrain and elevation data (DTED) and road map among others. The resulting coupled tracking and identification system is expected to outperform the separately designed systems particularly during target maneuvers, for recovering from temporary data dropout, and in a dense target environment.

关键词： Sensor data fusion

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Image fusion techniques for robust inspection of specular surfaces

Image fusion techniques for robust inspection of specular su...

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multisensor. multisource information fusion: architectures, algorithms, and applications 2003

作者： León, Fernando Puente Kammel, Sören Lehrstuhl Messsystem- Sensotechnik Technische Universitat Munchen D-80290 München Germany Postfach 6980 D-76128 Karlsruhe Germany

This contribution presents a new fusion strategy to inspect specular surfaces. To cope with illumination problems, several images are recorded with different lighting. Typically, the information of interest is extracted from each image separately and is then combined at a decision level. However, in our approach all images are processed simultaneously by means of a centralized fusion - no matter whether the desired results are images, features or symbols. Since the information fused is closer to the source, a better exploitation of the raw data is achieved. The sensors are virtual in the sense that a single camera is employed to record all images with different illumination patterns. The fusion problem is formulated by means of an energy function. Its minimization yields the desired fusion results, which describe surface defects. The performance of the proposed methodology is illustrated by means of two case studies: the analysis of machined surfaces, and the inspection of painted free-form surfaces. The programmable light sources utilized are a DMD, and an LED based illumination device, respectively. In both cases, the results demonstrate that by generating complementary imaging situations and using fusion techniques, a reliable yet cost-efficient inspection is attained matching the needs of industry.

关键词： Sensor data fusion

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A study of integration methods of aerial imagery and LIDAR data for high level of automation in 3D building reconstruction

A study of integration methods of aerial imagery and LIDAR d...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Seo, SY Schenk, T Ohio State Univ Dept Civil & Environm Engn & Geodet Sci Columbus OH 43210 USA

ISBN: (纸本)0819449598

This paper describes integration methods to increase the level of automation in building reconstruction. Aerial imagery has been used as a major source in mapping fields and, in recent years, LIDAR data became popular as another type of mapping resources. Regarding to their performances, aerial imagery has abilities to delineate object boundaries but leaves many missing parts of boundaries during feature extraction. LIDAR data provide direct information about heights of object surfaces but have limitation for boundary localization. Efficient methods using complementary characteristics of two sensors are described to generate hypotheses of building boundaries and localize the object features. Tree structures for grid contours of LIDAR data are used for interpretation of contours. Buildings are recognized by analyzing the contour trees and modeled with surface patches from LIDAR data. Hypotheses of building models are generated as combination of wing models and verified by assessing the consistency between the corresponding data sets. Experiments using aerial imagery and laser data are presented. Our approach shows that the building boundaries are successfully recognized through our contour analysis approach and the inference from contours and our modeling method using wing model increase the level of automation in hypothesis generation/verification steps.

关键词： aerial imagery LIDAR contour model wing model building recognition building reconstruction sensor fusion co-registration

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CB round discrimination fusing visible and infrared camera data

CB round discrimination fusing visible and infrared camera d...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Nelson, BN Birenzvige, A Underwood, W Sickenberger, D GEO CENTRES INC Newton Ctr MA 02459 USA

ISBN: (纸本)0819449598

In support of the Disparate Sensor Integration (DSI) Program a number of imaging sensors were fielded to determine the feasibility of using information from these systems to discriminate between chemical simulant and high explosives munitions. The imaging systems recorded video from 160 training and 100 blind munitions detonation events. Two types of munitions were used;155 mm high explosives rounds and 155 mm chemical simulant rounds. In addition two different modes of detonation were used with these two classes of munitions;detonation on impact (point detonation) and detonation prior to impact (airblasts). The imaging sensors fielded included two visible wavelength cameras, a near infrared camera, a mid wavelength infrared camera system and a long wavelength infrared camera system. Our work to date has concentrated on using the data from one of the visible wavelength camera systems and the long wavelength infrared camera system. The results provided in this paper clearly show the potential for discriminating between the two types of munitions and the two detonation modes using these camera data. It is expected that improved classification robustness will be achieved when the camera data described in this paper is combined with results and discriminating features generated from some of the other camera systems as well as the acoustic and seismic sensors also fielded in support of the DSI Program. The paper will provide a brief description of the camera systems and provide still imagery that show the four classes of explosives events at the same point in the munitions detonation sequence in both the visible and long wavelength infrared camera data. Next the methods used to identify frames of interest from the overall video sequence will be described in detail. This will be followed by descriptions of the features that are extracted from the frames of interest. A description of the system that is currently used for performing classification with the extracted feature

关键词： feature level fusion explosives event classification spectral band fusion image processing

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CB detection and early warning - Fusing disparate sensors into the detection process - Program overview

CB detection and early warning - Fusing disparate sensors in...

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Conference on multisensor, multisource information fusion - architectures, algorithms and applications 2003

作者： Birenzvige, A Sickenberger, D Reyes, F Underwood, W Reif, C Gonski, D Fargues, M Nelson, B USA SBCCOM Edgewood Chem & Biol Ctr Aberdeen Proving Ground MD 21010 USA

ISBN: (纸本)0819449598

Improved situational awareness is a primary goal for the Objective Force. Knowing where the enemy is and what are the threats to his troops provides the commander with the information he needs to plan his mission and provide his forces with maximum protection from the variety of threats that are present on the battlefield. Sensors play an important role in providing critical information to enhance situational awareness. The sensors that are used on the battlefield include, among others, seismic, acoustic, and cameras in different spectral ranges of the electromagnetic spectrum. These sensors help track enemy movement and serve as part of an intrusion detection system. Characteristically these sensors are relatively cheap and easy to deploy. Chemical and biological agent detection is currently relegated to sensors that are specifically designed to detect these agents. Many of these sensors are collocated with the troops. By the time alarm is sounded the troops have already been exposed to the agent. In addition, battlefield contaminants frequently interfere with the performance of these sensors and result in false alarms. Since operating in a contaminated environment requires the troops to don protective garments that interfere with their performance we need to reduce false alarms to an absolute minimum. The Edgewood Chemical and Biological Center (ECBC) is currently conducting a. study to examine the possibility of detecting chemical and biological weapons as soon as they are deployed. For that purpose we conducted a field test in which the acoustic, seismic and electro-magnetic signatures of conventional and simulated chemical / biological 155mm artillery shells were recorded by an array of corresponding sensors. Initial examination of the data shows a distinct differences in the signatures of these weapons. In this paper we will provide detailed descriptions of the test procedures. We will describe the various sensors used and describe the differences in the signa

关键词： battlefield sensors weapons discrimination sensor and data fusion

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