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Distributed multisensor fusion

Distributed multisensor fusion

引用

Guidance, Navigation, and Control conference, 1994

作者： Pao, Lucy Y. Electrical Engineering and Computer Science Department Northwestern University EvanstonIL60208 United States

There have been several algorithms proposed for multisensor tracking of multiple objects using a centralized processing architecture, but because of considerations such as reliability, survivability, and communication bandwidth, distributed processing architectures are often the only alternative. The distributed fusion problem is more complex than the centralized fusion problem due to correlation across track estimates for the same object. We propose an approach for distributed sensor fusion that bypasses this correlation problem and allows previously developed centralized fusion methods to be employed after a measurement reconstruction procedure. Limitations and assumptions are discussed, and simulation results are presented to demonstrate the performances obtained with this approach © 1994 American Institute of Aeronautics and Astronautics. Inc. All rights reserved.

关键词： Computer architecture

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DEVELOPMENT OF A FIELD-PORTABLE IMAGING-SYSTEM FOR SCENE CLASSIFICATION USING MULTISPECTRAL DATA fusion algorithms

DEVELOPMENT OF A FIELD-PORTABLE IMAGING-SYSTEM FOR SCENE CLA...

引用

IEEE 1994 National Aerospace and Electronics conference (NAECON 1994)

作者： PRESTON, E BERGMAN, T GORENFLO, R HERMANN, D KOPALA, E KUZMA, T LAZOFSON, L ORKIS, R BATTELLE MEM INST COLUMBUSOH 43201

ISBN: (纸本)0780318935

Battelle is developing a real-time multispectral imaging and classification system which can be taken into the field to support automated target/background discrimination. The system consists of a passive, multispectral imaging electro-optical sensor suite and a real-time digital data collection and data infusion image processor. The implemented algorithms include unsupervised maximum likelihood, Linde Buzo Gray, and fuzzy clustering algorithms along with Multilayer Perceptron and Learning Vector Quantization neural networks. Supervised clustering of the data was also used. Meanwhile, the system also has applications in environmental remote sensing, industrial inspection and medical imaging.

关键词： Imaging systems

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Unsupervised learning neural networks with applications to data fusion

Unsupervised learning neural networks with applications to d...

引用

Proceedings of the 1994 American Control conference. Part 1 (of 3)

作者： Wann, Chin-Der Thomopoulos, Stelios C.A. Pennsylvania State Univ University Park United States

An unsupervised learning artificial neural network, DIGNET, is used to design a multi-sensor data fusion system. DIGNET is a self-organizing neural network model with its system parameters analytically determined from self-organization during the learning process. The fast and stable clustering of DIGNET on statistical pattern recognition is used to supplement the decision making on multi-sensor detection problems. Features of the received signals are extracted by using signal processing techniques at each sensor stage before presented to data fusion. The data fusion architecture consists of DIGNET models and decision making algorithms. The function of DIGNET is to perform feature clustering prior to data fusion. The clusters of features created by DIGNET are fused by a decision making algorithm for an integrated decision. Experimental results in a multi-sensor moving target indication system show that data fusion with DIGNET successfully detects and tracks multiple moving targets embedded in clutter.

关键词： Neural networks

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EVALUATION OF THE MLANS applications TO NONCOOPERATIVE IFF 3

EVALUATION OF THE MLANS APPLICATIONS TO NONCOOPERATIVE IFF

引用

conference on Signal Processing, sensor fusion, and Target Recognition III

作者： CHERNICK, J PERLOVSKY, LI TYE, D USA MAT SYST ANAL ACTIVABERDEEN PROVING GROUNDMD 21010

ISBN: (纸本)0819415367

This paper describes applications of Maximum Likelihood Adaptive Neural System (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution;on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data. © 1994 SPIE. All rights reserved.

关键词： Maximum likelihood

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Unsupervised learning neural networks with applications to data fusion

Unsupervised learning neural networks with applications to d...

引用

American Control conference (ACC)

作者： Chin-Der Wann S.C.A. Thomopoulos Decision and Control Systems Laboratory Department of Electrical Engineering Pennsylvania State University University Park PA USA

关键词： Unsupervised learning Neural networks Artificial neural networks Decision making Signal processing algorithms Clustering algorithms Pattern recognition Signal processing Data mining sensor fusion

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Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms

Development of a field-portable imaging system for scene cla...

引用

IEEE National conference on Aerospace and Electronics (NAECON)

作者： E. Preston T. Bergman R. Gorenflo D. Hermann E. Kopala T. Kuzma L. Lazofson R. Orkis Bell Northern Research Limited Columbus OH USA

Battelle scientists are involved in the development of a real-time multispectral imaging and classification system which can be taken into the field to support automated target/background discrimination. The system also has applications in environmental remote sensing, industrial inspection and medical imaging. The Battelle-developed system consists of a passive, multispectral imaging Electro-Optical (E-O) sensor suite and a real-time digital data collection and data fusion image processor. The E-O sensor suite, able to collect imagery in 12 distinct wavebands from the ultraviolet (UV) through the long wave infrared (LWIR), consists of five charge-coupled device (CCD) cameras and two thermal IR imagers integrated on a common platform. The data collection and processing system consists of video switchers, recorders and a real-time sensor fusion/classification hardware system which takes any three wavebands as input and fuses the bands together by applying look-up tables, derived from tailored neural network algorithms, to classify the scene. The result is then visualized in a video format on a full color, 9-inch, active matrix Liquid Crystal Display (LCD). A variety of classification algorithms including artificial neural networks and data clustering techniques were successfully optimized to perform pixel-level classification of imagery in complex scenes. The optimized classification algorithm is used to populate the look-up tables in the real-time sensor fusion board for use in the field.< >

关键词： Layout Real time systems sensor fusion Clustering algorithms Multispectral imaging Charge-coupled image sensors Infrared image sensors Artificial neural networks Classification algorithms Remote sensing

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SCENE CLASSIFICATION AND SEGMENTATION USING MULTISPECTRAL sensor fusion IMPLEMENTED WITH NEURAL NETWORKS 4

SCENE CLASSIFICATION AND SEGMENTATION USING MULTISPECTRAL SE...

引用

4th International conference on applications of Artificial Neural Networks

作者： LAZOFSON, LE KUZMA, TJ BATTELLE MEM INST COLUMBUSOH 43201

ISBN: (纸本)0819412015

Near-simultaneous, multispectral, coregistered imagery of ground target and background signatures were collected over a full diurnal cycle in the MWIR, LWIR, near-infrared, blue, green, and red wavebands using Battelle's portable sensor suite. The imagery data were processed with classical statistical algorithms and artificial neural networks to discriminate target signatures from background clutter and investigate automatic target detection and recognition schemes.

关键词： Neural networks Image processing Image segmentation Artificial neural networks Data processing Image fusion Detection and tracking algorithms Network architectures sensor fusion sensors

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IMAGE-BASED TARGET TRACKING - fusion ARCHITECTURE AND sensor MANAGEMENT 2

IMAGE-BASED TARGET TRACKING - FUSION ARCHITECTURE AND SENSOR...

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CONF ON SIGNAL PROCESSING, sensor fusion, AND TARGET RECOGNITION 2

作者： HERSEN, B LALO, A LANEUVILLE, D MARITON, M MATRA MS2i (France)

ISBN: (纸本)0819411914

Following the acceptance of the linear Gauss Markov paradigm pioneered by Kalman, the engineering practice for the design of target tracking applications had been maturing over the last two decades. In recent years however two emerging facts have called for a renewed attention from the research community: (1) the generalization of multiple sensor architectures, motivated by higher requirements in terms of target description and robustness to electronic warfare, and (2) the availability of affordable imaging sensors, following progress in infrared detectors technology. The purpose of this communication is to report on some recent work addressing the issues raised by these two new aspects of tracking application design. Ideas are illustrated using an air defense scenario.

关键词： sensors Optical sensors Detection and tracking algorithms Environmental sensing Image processing Defense and security Image fusion Image sensors Imaging systems sensor fusion

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REAL-TIME COMPUTATIONAL NEEDS OF A MULTIsensor FEATURE-BASED RANGE-ESTIMATION METHOD

REAL-TIME COMPUTATIONAL NEEDS OF A MULTISENSOR FEATURE-BASED...

引用

CONF ON sensor fusion AND AEROSPACE applications

作者： SUORSA, RE SRIDHAR, B FONG, TW NASA Ames Research Ctr. (United States) Recom Technologies Inc. (United States)

ISBN: (纸本)0819411922

The computer vision literature describes many methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. Methods may be broadly categorized into field-based techniques and feature-based techniques. Field-based techniques have the advantage of regular computational structure at every pixel throughout the image plane. Feature-based techniques are much more data driven in that computational complexity increases dramatically in regions of the image populated by features. It is widely believed that to run computer vision algorithms in real time a parallel architecture is necessary. Field-based techniques lend themselves to easy parallelization due to their regular computational needs. However, we have found that field-based methods are sensitive to noise and have traditionally been difficult to generalize to arbitrary vehicle motion. Therefore, we have sought techniques to parallelize feature-based methods. This paper describes the computational needs of a parallel feature-based range-estimation method developed by NASA Ames. Issues of processing-element performance, load balancing, and data-flow bandwidth are addressed along with a performance review of two architectures on which the feature-based method has been implemented.

关键词： Image processing Detection and tracking algorithms sensor fusion Aerospace engineering IRIS Consortium sensors Image fusion Image sensors Silicon Visualization

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On-platform radar and optical track fusion

On-platform radar and optical track fusion

引用

Proceedings of the 27th Asilomar conference on Signals, Systems & Computers

作者： Sorell, Matthew J. Krieg, Mark Stewart, Clayton V. George Mason Univ Fairfax United States

ISBN: (纸本)0818641207

The Australian Defence Science and Technology Organization is developing a single-platform sensor fusion testbed based around an experimental X-band generic pulse Doppler radar. Initial research will examine real-time fusion of amplitude monopulse radar azimuth and elevation and video position estimates and the tracking ability of the combined sensor system. The addition of further signal processing and sensors will allow experimental verification of a variety of sensor fusion and management algorithms. Examples of preliminary data are shown and the continuing development of the test-bed and its applications are discussed.

关键词： Radar

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