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检索条件"任意字段=Sensor Fusion: Architectures, Algorithms and Applications V"

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Investigation of target tracking performance for unattended ground sensor networks employing Distributed Cluster Management

Investigation of target tracking performance for unattended ...

引用

Conference on Multisensor, Multisource Information fusion

作者： Stelzig, Chad A. Essawy, Magdi A. Minor, Sharon Gen Dynam Adv Informat Syst 8800 Queen Ave S Bloomington MN 55431 USA US Army CECOM Ctr Night Vis & Electroopt Ft Belvoir VA 22060 USA

ISBN: (纸本)0819462985

Interest in the distribution of processing in unattended ground sensing (UGS) networks has resulted in new technologies and system designs targeted at reduction of communication bandwidth and resource consumption through managed sensor interactions. A successful management algorithm should not only address the conservation of resources, but also attempt to optimize the information gained through each sensor interaction so as to not significantly deteriorate target tracking performance. This paper investigates the effects of Distributed Cluster Management (DCM) on tracking performance when operating in a deployed UGS cluster. Originally designed to reduce communications bandwidth and allow for sensor field scalability, the DCM has also been shown to simplify the target tracking problem through reduction of redundant information. It is this redundant information that in some circumstances results in secondary false tracks due to multiple intersections and increased uncertainty during track initiation periods. A combination of field test data playback and Monte Carlo simulations are used to analyze and compare the performance of a distributed UGS cluster to that of an unmanaged centralized cluster.

关键词： unattended ground sensors Distributed Cluster Management target tracking

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Data fusion cm a distributed heterogeneous sensor network

Data fusion cm a distributed heterogeneous sensor network

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2006

作者： Lamborn, Peter Williams, Pamela J. Mississippi State University 302 B Park Circle Place Starkville MS 39759 United States Sandia National Laboratories MS9159 P.O. Box 969 Livermore CA 94551-0969 United States

ISBN: (纸本)0819462985

Alarm-based sensor systems are being explored as a tool to expand perimeter security for facilities and force protection. However, the collection of increased sensor data has resulted in an insufficient solution that includes faulty data points. Data analysis is needed to reduce nuisance and false alarms, which will improve officials' decision making and confidence levels in the system's alarms. Moreover, operational costs can be allayed and losses mitigated if authorities are alerted only when a real threat is detected. In the current system, heuristics such as persistence of alarm and type of sensor that detected an event are used to guide officials' responses. We hypothesize that fusing data from heterogeneous sensors in the sensor field can provide more complete situational awareness than looking at individual sensor data. We propose a two stage approach to reduce false alarms. First, we use self organizing maps to cluster sensors based on global positioning coordinates and then train classifiers on the within cluster data to obtain a local view of the event. Next, we train a classifier on the local results to compute a global solution. We investigate the use of machine learning techniques, such as k-nearest neighbor, neural networks, and support vector machines to improve alarm accuracy. On simulated sensor data, the proposed approach identifies false alarms with greater accuracy than a weighted voting algorithm.

关键词： sensor data fusion

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Scalable algorithms for aggregating disparate forecasts of probability

Scalable algorithms for aggregating disparate forecasts of p...

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9th International Conference on Information fusion

作者： Predd, J. B. Kulkarni, S. R. Poor, H. v. Osherson, D. N. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Princeton Univ Dept Psychol Princeton NJ 08544 USA

ISBN: (纸本)9781424409532

In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for fusing large corpora of internally incoherent probability assessments. The algorithm is characterized by a provable performance guarantee, and is demonstrated to be orders of magnitude faster than existing tools when tested on several real-world data-sets. In addition, unexpected connections between research in risk assessment and wireless sensor networks are exposed, as several key ideas are illustrated to be useful in both fields.

关键词： aggregation forecasting fusion risk assessment sensor networks

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Pedestrian detection by Multi-spectral fusion

Pedestrian detection by Multi-spectral fusion

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Conference on Multisensor, Multisource Information fusion

作者： Ma, Yunqian Wang, Zheng Bazakos, Mike Honeywell Labs 3660 Technol Dr Minneapolis MN 55418 USA Honeywell Technol Solut Lab Shanghai 201203 Peoples R China

ISBN: (纸本)0819462985

Security systems increasingly rely on the use of Automated video Surveillance (AvS) technology. In particular the use of digital video renders itself to internet and local communications, remote monitoring, and to computer processing. AvS systems can perform many tedious and repetitive tasks currently performed by trained security personnel. AvS technology has already made some significant steps towards automating some basic security functions such as: motion detection, object tracking and event-based video recording. However, there are still many problems associated with just these automated functions, which need to be addressed further. Some examples of these problems are: the high "false alarm rate" and the "loss of track" under total or partial occlusion, when used under a wide range of operational parameters (day, night, sunshine, cloudy, foggy, range, viewing angle, clutter, etc.). Current surveillance systems work well only under a narrow range of operational parameters. Therefore, they need be hardened against a wide range of operational conditions. In this paper, we present a Multi-spectral fusion approach to perform accurate pedestrian segmentation under varying operational parameters. Our fusion method combines the "best" detection results from the visible images and the "best" from the thermal images. Commonly, the motion detection results in the visible images are easily affected by noise and shadows. The objects in the thermal image are relatively stable, but they may be missing some parts of the objects, because they thermally blend with the background. Our method makes use of the "best" object components and de-emphasize the "not best".

关键词： pedestrian detection sensor fusion surveillance level set

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Proceedings of SPIE - The International Society for Optical Engineering: Introduction

Proceedings of SPIE - The International Society for Optical ...

引用

Proceedings of SPIE - The International Society for Optical Engineering 2006年 6242卷 ix-xi页

作者： Dasarathy, Belur v.

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Data fusion in multi sensor platforms for wide-area perception

Data fusion in multi sensor platforms for wide-area percepti...

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IEEE Symposium on Intelligent vehicle

作者： A. Polychronopoulos N. Floudas A. Amditis D. Bank B. van den Broek Institute of Communications and Computer Systems Athens Greece Daimler Chrysler Aerospace Ulm Germany TNO Defensie en Veiligheid Den Haag Netherlands

There is a strong belief that the improvement of preventive safety applications and the extension of their operative range are achieved by the deployment of multiple sensors with wide fields of view (FOv). The paper contributes to the solution of the problem and introduces distributed sensor data fusion architectures and algorithms for an efficient deployment of multiple sensors that give redundant or complementary information for the moving objects. The proposed fusion architecture is based on a modular approach allowing exchangeability and benchmarking using the output of individual trackers, whereas the fusion algorithm gives a solution to the track management problem and the coverage of wide perception areas. The test case is LATERAL SAFE sensor configuration, which monitors the rear and lateral areas of the vehicle. Results show that with the given approach the system is able to maintain the ID of all objects in transition (an object enters a sensor's FOv) and blind areas (no sensor coverage)

关键词： sensor fusion sensor systems Laser radar Infrared sensors vehicle safety Accidents sensor systems and applications Radar tracking Intelligent sensors Benchmark testing

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Scalable algorithms for Aggregating Disparate Forecasts of Probability

Scalable Algorithms for Aggregating Disparate Forecasts of P...

引用

International Conference on Information fusion

作者： J. B. Predd S. R. Kulkarni H. v. Poor D. N. Osherson Department of Electrical Engineering Princeton University Princeton NJ USA Department of Psychology Princeton University Princeton NJ USA

ISBN: (纸本)1424409535

关键词： Wireless sensor networks Risk management Signal processing algorithms Humans sensor arrays Projection algorithms Risk analysis Economic forecasting Robustness Iterative algorithms

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A new class of Heuristic Polynomial Time algorithms to solve the Multidimensional Assignment Problem

A new class of Heuristic Polynomial Time Algorithms to solve...

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International Conference on Information fusion

作者： F. Perea H. W. De Waard AWS Department Thales Naval Nederland B.V. Netherlands

ISBN: (纸本)1424409535

The multidimensional assignment problem (MAP) is a combinatorial optimization problem arising in many applications, for instance in multi-target multi-sensor tracking problems. It is well-known that the MAP is NP-hard. The objective of a MAP is to match d-tuples of objects in such a way that the solution with the optimum total cost is found. In this paper a new class of approximation algorithms to solve the MAP is presented, named K-SGTS, and its effectiveness in multi-target multi-sensor tracking situations is shown. Its computational complexity is proven to be polynomial. Experimental results on the accuracy and speed of K-SGTS are provided in the last section of the paper

关键词： Polynomials Heuristic algorithms Multidimensional systems Radar tracking Position measurement Target tracking State estimation Covariance matrix Clutter Radar measurements

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Development of a MATLAB Based Target Tracking Simulation Environment

Development of a MATLAB Based Target Tracking Simulation Env...

引用

IEEE Signal Processing and Communications applications (SIU)

作者： Aslan Saranli Baykal İLTAREN TÜBİTAK-UEKAE Ankara Turkey TÜBİTAK-UEKAE İLTAREN Ankara Elektrik ve Elektronik Müihendisliği Bölümü O.D.T.Ü Ankara Turkey Elektrik ve Elektronik Mühendisliği Bölümü O.D.T.Ü Ankara

A simulation environment for tracking of maneuvering targets in clutter is developed in MATLAB. The simulation environment allows to generate 2-dimensional surveillance radar measurements and to run various target tracking algorithms on these measurements. As a first simulation example, IMM-NNJPDA algorithm, which incorporates NNJPDA data association and IMM filter structure, is implemented and the performance of this algorithm is investigated in an example scenario. By this simulator, in the future, it is aimed that statistical test and evaluation of different radar sensors, scenarios, target tracking methods and data fusion architectures will be performed

关键词： MATLAB Target tracking Radar tracking Clutter Surveillance Radar measurements Filters Testing sensor fusion Performance evaluation

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Multiple-Hypothesis Trilateration and Tracking with Distributed Radars

Multiple-Hypothesis Trilateration and Tracking with Distribu...

引用

International Conference on Information fusion

作者： Jelle van Kleef Jeroen Bergmans Leon Kester Frans Groen TNO D&V TNO D & V Netherlands Informatics Institute University of Amsterdam Netherlands

ISBN: (纸本)1424409535

A novel algorithm to associate and trilaterate detections from multiple distributed radars is presented. The algorithm provides for flexible track state representations. The coordinate system of a track is switched from the measurement coordinates (range-Doppler) to cartesian coordinates when a detection from another sensor is associated to the track. In the case of multiple targets and false alarms we run into the complication of multiple association possibilities. These can be resolved by using a multi-hypothesis algorithm. In general, correctly formed tracks will have more likely associations. Therefore, hypotheses describing these tracks will be favored. Simulations with one or two targets and different false alarm rates show the need to preserve multiple hypotheses of the world state. Tracking performance for various false alarms rates is evaluated

关键词： Radar tracking Radar detection Target tracking Coordinate measuring machines Robustness Equations sensor systems Informatics Change detection algorithms Radar applications

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