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

作者： Johnson, Kevin Minor, Christian USN Res Lab Washington DC 20375 USA Nova Res Inc Alexandria VA 22308 USA

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented.

关键词： sensor fusion Bayes decision theory

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2012

Multisensor, Multisource Information Fusion: Architectures, ...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2012

ISBN: (纸本)9780819490858

This proceedings contains 27 papers. These conferences provide a general discussion and dissemination for researchers and designers of recent advances in field of Multisensor, Multisource Information fusion: architectures, algorithms, and applications. Papers published were categorized under seven sessions, i.e. Session 1: Information fusion Approaches and algorithms with 4 papers;Session 2: Information fusion Approaches and algorithms ii with 3 papers;Session 3: Information fusion in Robotics with 6 papers;Session 4: Information fusion Approaches and algorithms iiI (biometrics-related) with 3 papers;Session 5: Image fusion with 3 papers;Session 6: Information fusion Approaches and algorithms IV (human-in-the-loop) with 5 papers;Session 7: Information fusion Systems and Evaluation Measures with 2 papers. Besides, a paper was published as poster session. The key terms of the proceeding papers include sensor fusion;Intensity interferometry;Data Association;Intrusion protection;Network trace generators;Bayesian fusion;Layered sensing systems.

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Spectral and Spatial Algorithm Architecture for Classification of Hyperspectral and LIDAR

Spectral and Spatial Algorithm Architecture for Classificati...

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

作者： Rand, Robert S. Khuon, Timothy S. Natl Geospatial Intelligence Agcy Off Innovis Bas & Appl Res Springfield VA 22153 USA

ISBN: (纸本)9780819490858

Architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area.

关键词： sensor fusion Architecture Neural Net HSI LIDAR Classification Mean-shift Stochastic Expectation-Maximization Imagery Back-propagation Perceptron Probability of detection and false alarm

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Multirobot autonomous landmine detection using distributed multisensor information aggregation

Multirobot autonomous landmine detection using distributed m...

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

作者： Jumadinova, Janyl Dasgupta, Prithviraj Univ Nebraska Omaha NE 68182 USA

ISBN: (纸本)9780819490858

We consider the problem of distributed sensor information fusion by multiple autonomous robots within the context of landmine detection. We assume that different landmines can be composed of different types of material and robots are equipped with different types of sensors, while each robot has only one type of landmine detection sensor on it. We introduce a novel technique that uses a market-based information aggregation mechanism called a prediction market. Each robot is provided with a software agent that uses sensory input of the robot and performs calculations of the prediction market technique. The result of the agent's calculations is a 'belief' representing the confidence of the agent in identifying the object as a landmine. The beliefs from different robots are aggregated by the market mechanism and passed on to a decision maker agent. The decision maker agent uses this aggregate belief information about a potential landmine and makes decisions about which other robots should be deployed to its location, so that the landmine can be confirmed rapidly and accurately. Our experimental results show that, for identical data distributions and settings, using our prediction market-based information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques.

关键词： Prediction market information fusion multi-sensor aggregation landmine detection

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Agent-based analysis of trustworthiness in wireless sensor networks

Agent-based analysis of trustworthiness in wireless sensor n...

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

作者： Fernandes, Ronald Li, Biyan Vadakkeveedu, Kalyan Verma, Ajay Gustafson, Paul Hwang, Jong Knowledge Based Syst Inc 1408 Univ Dr E College Stn TX 77840 USA Air Force Lab Dayton OH 45433 USA

ISBN: (纸本)9780819490858

Information assurance is a critical component of any organization's data network. Trustworthiness of the sensor data, especially in the case of wireless sensor networks (WSNs), is an important metric for any application that requires situational awareness. In a WSN, information packets are typically not encrypted and the nodes themselves could be located in the open, leaving them susceptible to tampering and physical degradation. In order to develop a method to assess trustworthiness in WSNs, we have utilized statistical trustworthiness metrics and have implemented an agent-based simulation platform that can perform various trustworthiness measurement experiments for various WSN operating scenarios. Different trust metrics are used against multiple vulnerabilities to detect anomalous behavior and node failure as well as malicious attacks. The simulation platform simulates WSNs with various topologies, routing algorithms, battery and power consumption models, and various types of attacks and defense mechanisms. Additionally, we adopt information entropy based techniques to detect anomalous behavior. Finally, detection techniques are fused to provide various metrics, and various trustworthiness metrics are fused to provide aggregate trustworthiness for the purpose of situational awareness.

关键词： agent-based simulation trust and trustworthiness wireless sensor networks NetLogo simulation

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Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

Colour-the-INSight: combining a direct view rifle sight with...

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

作者： Hogervorst, M. A. Jansen, C. Toet, A. Bijl, P. Bakker, P. Hiddema, A. C. van Vliet, S. F. TNO Human Factors POB 23 NL-3769 GZ Soesterberg Netherlands Thales Netherland BV Huizen Netherlands Photonis Netherlands BV NL-9300 AB Groningen Netherlands Minist Def Joint Soldier & Equipment Ctr Expertise The Hague Netherlands

ISBN: (纸本)9780819490858

We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e. g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined

关键词： image fusion false colour natural colour mapping real-time fusion night vision

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Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

Human cognitive and perceptual factors in JDL level 4 hard/s...

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

作者： Rimland, Jeffrey C. Hall, David L. Graham, Jacob L. Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9780819490858

Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e. g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload. In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e. g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems.

关键词： data fusion soft data fusion participatory sensing tasking cognition perception human factors

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Reliable and energy-efficient cooperative routing algorithm for wireless monitoring systems

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IET WIRELESS sensor SYSTEMS 2012年第2期2卷 128-135页

作者： Manfredi, S. Univ Naples Federico II Fac Engn Via Claudio 21 I-80125 Naples Italy

The recent increased interest in distributed and flexible wireless pervasive applications has drawn great attention to Wireless Networked Control Systems (WNCS) architectures based on Wireless sensor and Actuator Networks (WSANs) and the resulting Quality of Service (QoS) obtained in specific applications. In wireless monitoring systems based on WSANs, providing certain QoS specifications in terms of reliability and energy efficiency is crucial for the sensors/actuators as they perform actions based on the data samples/received with a limited amount of energy to spend. To this aim the paper introduces the cooperative-based routing algorithm to guarantee a good performance trade-off between reliability and energy efficiency of the overall wireless monitoring system. Simulations have been carried out in order to quantify the impact of the proposed algorithm on the overall monitoring system reliability and energy efficiency and a comparison is presented with the existing Ad-hoc On-Distance Vector (AODV), the cooperation along the shortest non-cooperative path (CASNCP) and minimum-power cooperative routing (MPCR) algorithms. Finally it is shown the application of the proposed algorithm to healthcare monitoring system pointing out as the cooperation-based routing algorithms are suitable and rewarding for the management of the future generation of monitoring systems.

关键词： energy capacity Energy efficiency monitoring systems Routing algorithms wireless monitoring system wireless Quality of service

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Model predictive quadrotor control: attitude, altitude and position experimental studies

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IET CONTROL THEORY AND applications 2012年第12期6卷 1812-1827页

作者： Alexis, K. Nikolakopoulos, G. Tzes, A. Univ Patras Dept Elect & Comp Engn GR-26110 Patras Greece Lulea Univ Technol Comp Sci Elect & Space Engn Dept Lulea Sweden

This study addresses the control problem of an unmanned quadrotor in an indoor environment where there is lack of absolute localisation data. Based on an attached inertia measurement unit, a sonar and an optic-flow sensor, the state vector is estimated using sensor fusion algorithms. A novel switching model predictive controller is designed in order to achieve precise trajectory control, under the presence of forcible wind gusts. The quadrotor's attitude, altitude and horizontal linearised dynamics result in a set of piecewise affine models, enabling the controller to account for a larger part of the quadrotor's flight envelope while modelling the effects of atmospheric disturbances as additive-affine terms in the system. The proposed controller algorithm accounts for the state and actuation constraints of the system. The controller is implemented on a quadrotor prototype in indoor position tracking, hovering and attitude manoeuvres experiments. The experimental results indicate the overall system's efficiency in position/altitude/attitude set-point manoeuvres.

关键词： Location tracking Inertia measurement indoor environment GUSTS Attitude Atmospheric disturbances Controllers Predictive controllers sensor fusion trajectory control

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Using arm and hand gestures to command robots during stealth operations

Using arm and hand gestures to command robots during stealth...

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

作者： Stoica, Adrian Assad, Chris Wolf, Michael You, Ki Sung Pavone, Marco Huntsberger, Terry Iwashita, Yumi CALTECH Jet Prop Lab Pasadena CA 91109 USA

ISBN: (纸本)9780819490858

Command of support robots by the warfighter requires intuitive interfaces to quickly communicate high degree-of-freedom (DOF) information while leaving the hands unencumbered. Stealth operations rule out voice commands and vision-based gesture interpretation techniques, as they often entail silent operations at night or in other low visibility conditions. Targeted at using bio-signal inputs to set navigation and manipulation goals for the robot (say, simply by pointing), we developed a system based on an electromyography (EMG) "BioSleeve", a high density sensor array for robust, practical signal collection from forearm muscles. The EMG sensor array data is fused with inertial measurement unit (IMU) data. This paper describes the BioSleeve system and presents initial results of decoding robot commands from the EMG and IMU data using a BioSleeve prototype with up to sixteen bipolar surface EMG sensors. The BioSleeve is demonstrated on the recognition of static hand positions (e. g. palm facing front, fingers upwards) and on dynamic gestures (e. g. hand wave). In preliminary experiments, over 90% correct recognition was achieved on five static and nine dynamic gestures. We use the BioSleeve to control a team of five LANdroid robots in individual and group/squad behaviors. We define a gesture composition mechanism that allows the specification of complex robot behaviors with only a small vocabulary of gestures/commands, and we illustrate it with a set of complex orders.

关键词： Human-robot interfaces gesture recognition electromyography EMG sensor arrays stealth operations

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