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Real-Time Hardware-in-the-Loop Laboratory Testing for Multisensor Sense and Avoid Systems

INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 2013年第1期2013卷 9.1-9.9页

作者： Fasano, Giancarmine Accardo, Domenico Forlenza, Lidia Renga, Alfredo Rufino, Giancarlo Tancredi, Urbano Moccia, Antonio Univ Naples Federico II Dept Ind Engn DII I-80125 Naples Italy Univ Naples Parthenope Ctr Direz Dept Technol I-80143 Naples Italy

This paper focuses on a hardware-in-the-loop facility aimed at real-time testing of architectures and algorithms of multisensor sense and avoid systems. It was developed within a research project aimed at flight demonstration of autonomous non-cooperative collision avoidance for Unmanned Aircraft Systems. In this framework, an optionally piloted Very Light Aircraft was used as experimental platform. The flight system is based on multiple-sensor data integration and it includes a Ka-band radar, four electro-optical sensors, and two dedicated processing units. The laboratory test system was developed with the primary aim of prototype validation before multi-sensor tracking and collision avoidance flight tests. System concept, hardware/software components, and operating modes are described in the paper. The facility has been built with a modular approach including both flight hardware and simulated systems and can work on the basis of experimentally tested or synthetically generated scenarios. Indeed, hybrid operating modes are also foreseen which enable performance assessment also in the case of alternative sensing architectures and flight scenarios that are hardly reproducible during flight tests. Real-time multisensor tracking results based on flight data are reported, which demonstrate reliability of the laboratory simulation while also showing the effectiveness of radar/electro-optical fusion in a non-cooperative collision avoidance architecture.

关键词： REAL-time computing HARDWARE-in-the-loop simulation sensor networks COMPUTER architecture COMPUTER algorithms COLLISIONS (Nuclear physics) DRONE aircraft ELECTROOPTICAL devices

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Performance Analysis of Image fusion Methods in Transform Domain

Performance Analysis of Image Fusion Methods in Transform Do...

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

作者： Choi, Yoonsuk Sharifahmadian, Ershad Latifi, Shahram Univ Nevada Dept Elect & Comp Engn 4505 Maryland Pkwy Las Vegas NV 89154 USA

ISBN: (纸本)9780819495471

Image fusion involves merging two or more images in such a way as to retain the most desirable characteristics of each. There are various image fusion methods and they can be classified into three main categories: i) Spatial domain, ii) Transform domain, and iii) Statistical domain. We focus on the transform domain in this paper as spatial domain methods are primitive and statistical domain methods suffer from a significant increase of computational complexity. In the field of image fusion, performance analysis is important since the evaluation result gives valuable information which can be utilized in various applications, such as military, medical imaging, remote sensing, and so on. In this paper, we analyze and compare the performance of fusion methods based on four different transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled contourlet transform. fusion framework and scheme are explained in detail, and two different sets of images are used in our experiments. Furthermore, various performance evaluation metrics are adopted to quantitatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet transform method performs better than the other three methods. During the experiments, we also found out that the decomposition level of 3 showed the best fusion performance, and decomposition levels beyond level-3 did not significantly affect the fusion results.

关键词： Curvelet contourlet decomposition nonsubsampled contourlet wavelet

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Considerations for Multiple Hypothesis Correlation on Tactical Platforms

Considerations for Multiple Hypothesis Correlation on Tactic...

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

作者： Thomas, Alan M. Turpen, James E. Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30318 USA

ISBN: (纸本)9780819495471

Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered;however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth-limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete;system latencies can create temporal uncertainty in data;and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.

关键词： Data Association Multiple Hypothesis Correlation Multiple Hypothesis Tracking Avionics Information fusion sensors

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2013

Multisensor, Multisource Information Fusion: Architectures, ...

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2013

ISBN: (纸本)9780819495471

The proceedings contain 22 papers. The topics discussed include: affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness;considerations for multiple hypothesis correlation on tactical platforms;scalable sensor management for automated fusion and tactical reconnaissance;efficiently applying uncertain implication rules to the transferable belief model;a methodology for hard/soft information fusion in the condition monitoring of aircraft;using classifier fusion to improve the performance of multiclass classification problems;generalized information fusion and visualization using spatial voting and data modeling;real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall;spatial voting for automatic feature selection, fusion, and visualization;and fusion of data from multiple sensors with model-based data analysis.

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Enhancing situational awareness by means of visualization and information integration of sensor networks

Enhancing situational awareness by means of visualization an...

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

作者： Timonen, J. Vankka, J. Finnish Natl Def Univ Dept Mil Technol POB 7 FI-00861 Helsinki Finland

ISBN: (纸本)9780819495471

This paper presents a solution for information integration and sharing architecture, which is able to receive data simultaneously from multiple different sensor networks. Creating a Common Operational Picture (COP) object along with the base map of the building plays a key role in the research. The object is combined with desired map sources and then shared to the mobile devices worn by soldiers in the field. The sensor networks we used focus on location techniques indoors, and a simple set of symbols is created to present the information, as an addition to NATO APP6B symbols. A core element in this research is the MUSAS (Mobile Urban Situational Awareness System), a demonstration environment that implements central functionalities. Information integration of the system is handled by the Internet Connection Engine (Ice) middleware, as well as the server, which hosts COP information and maps. The entire system is closed, such that it does not need any external service, and the information transfer with the mobile devices is organized by a tactical 5 GHz WLAN solution. The demonstration environment is implemented using only commercial off-theshelf (COTS) products. We have presented a field experiment event in which the system was able to integrate and share real time information of a blue force tracking system, received signal strength indicator (RSSI) based intrusion detection system, and a robot using simultaneous location and mapping technology (SLAM), where all the inputs were based on real activities. The event was held in a training area on urban area warfare.

关键词： Common Operational Picture information integration urban warfare wireless sensor platform Mobile Urban Area Situational Awareness System Situational Awareness

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Analysis of Multi-sensor Attitude Measurement System on TBM

Analysis of Multi-sensor Attitude Measurement System on TBM

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6th International Conference on Intelligent Robotics and applications (ICIRA)

作者： Zhang, Chuncao Zhu, Guoli Zhang, Jianbo Pan, Minghua Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9783642408496;9783642408489

It is well known that the real-time high accuracy attitude measurement is required during the tunnel construction. The vibration is caused by hard rock tunneling, which makes the inclinometer fail to work, and then the current system can not meet the demand of auto-guide control. In this paper, we describe the fusion and test of an optic fiber gyroscope and inclinometer based on multi-sensor fusion algorithm. The proposed multi-sensor is used for measuring the attitude of TBM(Tunnel Boring Machine), which can solve those problems. Inclinometer has the advantage of high accuracy output and gyro is not sensitive to vibrations. The system discusses fuse information from two sensors and develops the fusion algorithms. Firstly, stochastic error model of gyro is built on the basis of ARMA(auto regressive moving average) modeling. Secondly, the detail modeling of fuse information for multi-sensor is made by using Kalman filter, which estimates and compensates zero drift of gyro with the high accurate output of inclinometer. The attitude under the strong vibration is calculated by compensated gyro. Finally, using test results, it is shown that the multisensor information fusion algorithm is effective and improves the accuracy of attitude measurement. It ensures that the error of multi-sensor attitude measurement system is less than 2 milliradian under the static and dynamic state.

关键词： Multi-sensor fusion Inclinometer Fiber optic gyroscope Kalman filtering TBM

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Visual and Inertial Multi-Rate Data fusion for Motion Estimation via Pareto-Optimization

Visual and Inertial Multi-Rate Data Fusion for Motion Estima...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Loianno, Giuseppe Lippiello, Vincenzo Fischione, Carlo Siciliano, Bruno Univ Naples Federico II PRISMA Lab Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

ISBN: (纸本)9781467363587

Motion estimation is an open research field in control and robotic applications. sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

关键词： Kalman filters Pareto optimisation computational complexity motion estimation robots sensor fusion statistical analysis

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Visual and Inertial Multi-Rate Data fusion for Motion Estimation via Pareto-Optimization

Visual and Inertial Multi-Rate Data Fusion for Motion Estima...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Giuseppe Loianno Vincenzo Lippiello Carlo Fischione Bruno Siciliano PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II via Claudio 21 80125 Naples Italy ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467363563

关键词： Motion Estimation via Pareto-Optimization control robotic applications motion estimation Pareto optimisation Inertial Visualization Standing Orders Data fusion data integration Kalman filters computer vision complexity classes

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Computationally efficient Bayesian tracking

Computationally efficient Bayesian tracking

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

作者： Aughenbaugh, Jason La Cour, Brian Univ Texas Austin Appl Res Labs Austin TX 78712 USA

ISBN: (纸本)9780819490858

In this paper, we describe the progress we have achieved in developing a computationally efficient, grid-based Bayesian fusion tracking system. In our approach, the probability surface is represented by a collection of multidimensional polynomials, each computed adaptively on a grid of cells representing state space. Time evolution is performed using a hybrid particle/grid approach and knowledge of the grid structure, while sensor updates use a measurement-based sampling method with a Delaunay triangulation. We present an application of this system to the problem of tracking a submarine target using a field of active and passive sonar buoys.

关键词： Heterogeneous sensor fusion target tracking Bayesian fusion

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Leveraging Provenance to Improve Data fusion in sensor Networks

Leveraging Provenance to Improve Data Fusion in Sensor Netwo...

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

作者： Dogan, Gulustan Seo, Eunsoo Brown, Theodore Abdelzaher, Tarek F. CUNY New York NY 10021 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9780819490858

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations.

关键词： Provenance Data fusion sensor Networks

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