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Wireless sensors information fusion for structural health monitoring

Wireless sensors information fusion for structural health mo...

引用

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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The application of Dempster-Shafer theory for landmine detection

The application of Dempster-Shafer theory for landmine detec...

引用

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

作者： Mudigonda, NR Kacelenga, R Erickson, D Gen Dynam Canada Ltd Calgary AB T2E 8P2 Canada

ISBN: (纸本)0819449598

This paper explores the concepts of multisensor data fusion based on the Dempster-Shafer (DS) evidential theory in order to achieve a mine versus false-alarm (FA) classification of landmine targets. Initially, a decision-level DS algorithm is proposed to combine the evidence from multiple sensors of the landmine detection system developed by the General Dynamics Canada Limited (GD Canada). Subsequently, a feature-level DS fusion algorithm is employed to operate on a set of features reported by the ground penetrating radar (GPR) sensor of the system. The data used in the present study was acquired from the Aberdeen Proving Grounds (APG) test site in USA as part of the Ground Standoff Mine Detection System (GSTAMIDS) trials. The proposed decision-level DS algorithm yielded a probability of detection (Pd) of 92.53% at a false-alarm rate (FAR) value of 0.0697 FAs/m(2). The Pd and the FAR performance results achieved by using the decision-level DS algorithm are comparable with the results obtained using three other decision-level fusion algorithms that were previously developed by GD, Canada based on heuristic, Bayesian inference, and Voting fusion concepts. On the other hand, the feature-level DS fusion, when tested with the information presented by the GPR sensor only, resulted in a higher Pd value of 78.54% as compared to the corresponding result of 61.43% obtained by using the heuristic algorithm. The GPR sensor is one of the three scanning sensors present on the system.

关键词： landmine detection multisensor data fusion Dempster-Shafer theory Bayesian theory voting fusion

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Use of multi sensor fusion technology to meet the challenges of emerging EO & RF threats to a combat aircraft

Use of multi sensor fusion technology to meet the challenges...

引用

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

作者： Shukla, AK Parthasarathy, T Rao, PNAP Govt India Minist Def Aeronaut Dev Agcy Bangalore 560017 Karnataka India

ISBN: (纸本)0819449598

The pilot on-board a combat aircraft encounters during any mission a dynamically varying threat environment of diverse EO and RF threats. Different sensors are carried on-board the aircraft to combat these threats. However, these sensors have their own limitations and no single sensor is able to perform in all kinds of situations. In addition, the technological advances in the threat scenario - in terms of higher speeds, small signatures and multimode guidance - and increased complex threats in the battlefield leading to generation of large amount of data input to the pilot make his decision making task very difficult due to increased workload. These challenges can be efficiently handled by deployment of a system on-board the aircraft with a comprehensive goal of autonomous target detection and tracking, situation and threat assessment and decision making based on multi-sensor data fusion techniques. In this paper, major emerging EO and RF threats for a combat aircraft and some important EO and RF sensors on-board the aircraft have been discussed. A design approach for the development of a multi-sensor data fusion system for a combat aircraft to provide better threat assessment than that provided by any single stand alone sensor has also been presented.

关键词： Multi-sensor Data fusion Electro-Optical (EO) Radio Frequency (RF) Moving Target Image (MTTM) Multiple Hypothesis Tracking (MHT) Probabilistic Data Association (PDA)

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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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Multi-level image fusion

Multi-level image fusion

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

作者： Petrovic, V Orasys MV YU-11077 Novi Beograd Serbia Yugoslavia

ISBN: (纸本)0819449598

Signal-level image fusion has in recent years established itself as a useful tool for dealing with vast amounts of image data obtained by disparate sensors. In many modem multisensor systems, fusion algorithms significantly reduce the amount of raw data that needs to be presented or processed without loss of information content as well as provide an effective way of information integration. One of the most useful and widespread applications of signal-level image fusion is for display purposes. Fused images provide the observer with a more reliable and more complete representation of the scene than would be obtained through single sensor display configurations. In recent years, a plethora of algorithms that deal with the problem of fusion for display has been proposed. However, almost all are based on relatively basic processing techniques and do not consider information from higher levels of abstraction. As some recent studies have shown this does not always satisfy the complex demands of a human observer and a more subjectively meaningful approach is required. This paper presents a fusion framework based on the idea that subjectively relevant fusion could be achieved if information at higher levels of abstraction such as image edges and image segment boundaries are used to guide the basic signal-level fusion process. fusion of processed, higher level information to form a blueprint for fusion at signal level and fusion of information from multiple levels of extraction into a single fusion engine are both considered. When tested on two conventional signal-level fusion methodologies, such multi-level fusion structures eliminated undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness. Images produced by inclusion of multi-level information in the fusion process are clearer and of generally better quality than those obtained through conventional low-level fusion. This is verified through subjective evaluatio

关键词： image fusion signal-level fusion feature-level fusion multi-level fusion

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Multisensor data fusion technologies in counterchecking disturbed targets

Multisensor data fusion technologies in counterchecking dist...

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Multisensor. Multisource Information fusion: architectures, algorithms, and applications 2003

作者： Wang, Ju Wu, Siliang Dept. of Electronics Engineering Beijing Institute of Technology Beijing 100081 China

The technique of data fusion is a new kind of data processing technique and has broad appliance foreground. Appearance of stealthy and disturbed targets in modern battlefield leads to data extraction and fusion by networking various sensors to maximize use of information to perform recognition, orientation and tracking of targets. As we known well, radar only acquire angle information without range information when encounter noise suppress jammer. Netted radars could get position information of interference by make use of radars position relatively and target angle information. Yet if there is more than one target, the orientation of targets could error due to orientation confuse. The paper present methods that through association arithmetic of kalman filter to avoid confuse.

关键词： sensor data fusion

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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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Integration of language and sensor information

Integration of language and sensor information

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

作者： Perlovsky, LI Weijers, B Air Force Res Lab Bedford MA 01731 USA

ISBN: (纸本)0819449598

The talk describes the development of basic technologies of intelligent systems fusing data from multiple domains and leading to automated computational techniques for understanding data contents. Understanding involves inferring appropriate decisions and recommending proper actions, which in turn requires fusion of data and knowledge about objects, situations, and actions. Data might include sensory data, verbal reports, intelligence intercepts, or public records, whereas knowledge ought to encompass the whole range of objects, situations, people and their behavior, and knowledge of languages. In the past, a fundamental difficulty in combining knowledge with data was the combinatorial complexity of computations, too many combinations of data and knowledge pieces had to be evaluated. Recent progress in understanding of natural intelligent systems, including the human mind, leads to the development of neurophysiologically motivated architectures for solving these challenging problems, in particular the role of emotional neural signals in overcoming combinatorial complexity of old logic-based approaches. Whereas past approaches based on logic tended to identify logic with language and thinking, recent studies in cognitive linguistics have led to appreciation of more complicated nature of linguistic models. Little is known about the details of the brain mechanisms integrating language and thinking. Understanding and fusion of linguistic information with sensory data represent a novel challenging aspect of the development of integrated fusion systems. The presentation will describe a non-combinatorial approach to this problem and outline techniques that can be used for fusing diverse and uncertain knowledge with sensory and linguistic data.

关键词： information fusion language sensors intelligent systems understanding linguistic models knowledge non-combinatorial cognitive neural

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Object aggregation using Neyman Pearson analysis

Object aggregation using Neyman Pearson analysis

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Multisensor. Multisource Information fusion: architectures, algorithms, and applications 2003

作者： Bai, Li Hinman, Michael Computer Fusion Laboratory Electrical and Engineering Dept. Temple University Temple PA 19122 United States IFE AFRL Rome Research Sites Rome NY 13441 United States

This paper presents a novel approach to: 1) distinguish military vehicle groups, and 2) identify names of military vehicle convoys in the level-2 fusion process. The data is generated from a generic Ground Moving Target Indication (GMTI) simulator that utilizes Matlab and Microsoft Access. This data is processed to identify the convoys and number of vehicles in the convoy, using the minimum timed distance variance (MTDV) measurement. Once the vehicle groups are formed, convoy association is done using hypothesis techniques based upon Neyman Pearson (NP) criterion. One characteristic of NP is the low error probability when a-priori information is unknown. The NP approach was demonstrated with this advantage over a Bayesian technique.

关键词： sensor data 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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