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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications"

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Sensitivity of fusion performance to classifier model variations

Sensitivity of fusion performance to classifier model variat...

引用

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

作者： Goebel, K GE Global Res Niskayuna NY 12309 USA

ISBN: (纸本)0819449598

During design of classifier fusion tools, it is important to evaluate the performance of the fuser. In many cases, the output of the classifiers needs to be simulated to provide the range of fusion input that allows an evaluation throughout the design space. One fundamental question is how the output should be distributed, in particular for multi-class continuous output classifiers. Using the wrong distribution may lead to fusion tools that are either overly optimistic or otherwise distort the outcome. Either case may lead to a fuser that performs sub-optimal in practice. It is therefore imperative to establish the bounds of different classifier output distributions. In addition, one must take into account the design space that may be of considerable complexity. Exhaustively simulating the entire design space may be a lengthy undertaking. Therefore, the simulation has to be guided to populate the relevant areas of the design space. Finally, it is crucial to quantify the performance throughout the design of the fuser. This paper addresses these issues by introducing a simulator that allows the evaluation of different classifier distributions in combination with a design of experiment setup, and a built-in performance evaluation. We show results from an application of diagnostic decision fusion on aircraft engines.

关键词： classification diagnostics information fusion decision fusion simulation

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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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A study of integration methods of aerial imagery and LIDAR data for high level of automation in 3D building reconstruction

A study of integration methods of aerial imagery and LIDAR d...

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

作者： Seo, SY Schenk, T Ohio State Univ Dept Civil & Environm Engn & Geodet Sci Columbus OH 43210 USA

ISBN: (纸本)0819449598

This paper describes integration methods to increase the level of automation in building reconstruction. Aerial imagery has been used as a major source in mapping fields and, in recent years, LIDAR data became popular as another type of mapping resources. Regarding to their performances, aerial imagery has abilities to delineate object boundaries but leaves many missing parts of boundaries during feature extraction. LIDAR data provide direct information about heights of object surfaces but have limitation for boundary localization. Efficient methods using complementary characteristics of two sensors are described to generate hypotheses of building boundaries and localize the object features. Tree structures for grid contours of LIDAR data are used for interpretation of contours. Buildings are recognized by analyzing the contour trees and modeled with surface patches from LIDAR data. Hypotheses of building models are generated as combination of wing models and verified by assessing the consistency between the corresponding data sets. Experiments using aerial imagery and laser data are presented. Our approach shows that the building boundaries are successfully recognized through our contour analysis approach and the inference from contours and our modeling method using wing model increase the level of automation in hypothesis generation/verification steps.

关键词： aerial imagery LIDAR contour model wing model building recognition building reconstruction sensor fusion co-registration

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Discrimination of Chemical/Biological versus high explosive artillery rounds using acoustic and seismic data fusion

Discrimination of Chemical/Biological versus high explosive ...

引用

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

作者： Reiff, C Fargues, M Gonski, D US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)0819449598

The threat of chemical and biological weapons is a serious problem and the ability to determine. if an incoming artillery round contains high explosives or a chemical/biological agent is valuable information to anyone on the battlefield. Early detection of a chemical or biological agent provides the soldier with more time to respond to the threat. Information about the round type and location can be obtained from acoustic and seismic sensors and fused with information from radars, infrared and video cameras, and meteorological sensors to identify the round type quickly after detonation. This paper will describe the work with ground based acoustic and seismic sensors to discriminate between round types in a program sponsored-by the Soldier Biological and Chemical Command.

关键词： chemical biological artillery discrimination data fusion acoustic seismic

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A neurophysiological paradigm for data fusion in a multisource environment

A neurophysiological paradigm for data fusion in a multisour...

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

作者： Fisher, PS Minton, DH Fisher, HP Winston Salem State Univ Dept Comp Sci Winston Salem NC 27110 USA

ISBN: (纸本)0819449598

A technique for representing data obtained from sensors, video streams. imagery, sound, text, etc. is presented. The technique is called Finite Inductive Sequences (FI) and is proposed as a means for eliminating data requiring storage where conventional mathematical models don't eliminate enough and statistical models eliminate too much. FI is a simple idea and is based upon a symbol push-out technique that allows the order (inductive base) of the model to be set to an a priori value for all derived rules. The rules are obtained from an exemplar data set, and are derived by a technique called factoring, and this results in a table of rules called a ruling. These rules can then be used in pattern recognition applications. These techniques are shown be example as well as a more formal setting, and lastly these rules and ruling are likened to the structure both present and absent in the cerebellum.

关键词： pattern recognition cerebellum data models finite induction data storage finite induction

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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

引用

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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Multi-channel nonlinear phase analysis for time frequency data fusion

Multi-channel nonlinear phase analysis for time frequency da...

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

作者： Mavandadi, S Aarabi, P Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

ISBN: (纸本)0819449598

A general method for time delay of arrival (TDOA) estimation for time-frequency information fusion is analyzed. This technique, for which the generalized cross correlation method and histogram methods are special cases, results in a low TDOA estimation error And high efficiency in computation. The proposed method relies on a non-linear phase-error selector function, which acts as a reward and punish method for the phase error at each frequency. Three different selector function candidates, consisting of cosine, rectangular, and triangular functions are analyzed using simulations. In the presence of Gaussian noise, the rectangular selector function performs better than the cosine at signal-to-noise ratios (SNRs) higher than 10dB while for lower SNRs the cosine function performs better. With speech noise, the cosine function, which corresponds to the generalized cross correlation technique, has higher anomaly percentages and higher root-mean-square errors than the rectangular function. This suggests, that in general, the rectangular selector function, which can be computed more easily than the cosine selector function, is superior technique to the generalized cross correlation method for. real-time applications.

关键词： delay-of-arrival estimation time-frequency data fusion microphone arrays speech processing

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CB round discrimination fusing visible and infrared camera data

CB round discrimination fusing visible and infrared camera d...

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

作者： Nelson, BN Birenzvige, A Underwood, W Sickenberger, D GEO CENTRES INC Newton Ctr MA 02459 USA

ISBN: (纸本)0819449598

In support of the Disparate sensor Integration (DSI) Program a number of imaging sensors were fielded to determine the feasibility of using information from these systems to discriminate between chemical simulant and high explosives munitions. The imaging systems recorded video from 160 training and 100 blind munitions detonation events. Two types of munitions were used;155 mm high explosives rounds and 155 mm chemical simulant rounds. In addition two different modes of detonation were used with these two classes of munitions;detonation on impact (point detonation) and detonation prior to impact (airblasts). The imaging sensors fielded included two visible wavelength cameras, a near infrared camera, a mid wavelength infrared camera system and a long wavelength infrared camera system. Our work to date has concentrated on using the data from one of the visible wavelength camera systems and the long wavelength infrared camera system. The results provided in this paper clearly show the potential for discriminating between the two types of munitions and the two detonation modes using these camera data. It is expected that improved classification robustness will be achieved when the camera data described in this paper is combined with results and discriminating features generated from some of the other camera systems as well as the acoustic and seismic sensors also fielded in support of the DSI Program. The paper will provide a brief description of the camera systems and provide still imagery that show the four classes of explosives events at the same point in the munitions detonation sequence in both the visible and long wavelength infrared camera data. Next the methods used to identify frames of interest from the overall video sequence will be described in detail. This will be followed by descriptions of the features that are extracted from the frames of interest. A description of the system that is currently used for performing classification with the extracted feature

关键词： feature level fusion explosives event classification spectral band fusion image processing

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Multiple algorithm fusion in a corrosion detection algorithm used with shipboard tank and void inspection systems

Multiple algorithm fusion in a corrosion detection algorithm...

引用

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

作者： Nelson, BN Slebodnick, P Lemieux, EJ CEO CENTERS Inc Newton Ctr MA 02459 USA

ISBN: (纸本)0819449598

Over the last several years the Naval Research Laboratory has developed video based systems for inspecting tanks (ballast, potable water, fuel, etc.) and other voids on ships. Over this past year, we have extensively utilized the Insertable Stalk Inspection System (ISIS) to perform inspections of shipboard tanks and voids. This system-collects between 15 and 30 images of the tank or void being inspected as well as a video archive of the complete inspection process. A corrosion detection algorithm analyzes the collected imagery. The corrosion detection algorithm output is the percent coatings damage in the tank being inspected. The corrosion detection algorithm consists of four independent algorithms that each separately assesses the coatings damage in each of the images that are analyzed. The algorithm results are fused to attain a single coatings damage value for each of the analyzed images. The damage values for each of the images are next aggregated in order to develop a single coatings damage value for the tank being inspected. This paper concentrates on the methods used to fuse the results from the four independent algorithms that assess corrosion damage at the individual image level as well as the methods used to aggregate the results from multiple images to attain a single coatings damage level. Results from both calibration tests and double blind testing are provided in the paper to demonstrate the advantages of the video inspection systems and the corrosion detection algorithm.

关键词： information fusion image processing automatic corrosion detection

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