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

共 329 条记录，以下是101-110 订阅

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Population bias control for bagging k-NN experts

Population bias control for bagging <i>k</i>-<i>NN</i> exper...

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Alkoot, FM Kittler, J Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)0819440809

We investigate bagging of k - NN classifiers under varying set sizes. For certain set sizes bagging often under-performs due to population bias. We propose a modification to the standard bagging method designed to avoid population bias. The modification leads to substantial performance gains, especially under very small sample size conditions. The choice of the modification method used depends on whether prior knowledge exists or not. If no prior knowledge exists then insuring that all classes exist in the bootstrap set yields the best results.

关键词： bagging k-nearest neighbour modified product fusion bootstrap population bias

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Fault detection and multi-classifier fusion for unmanned aerial vehicles (UAVs)

Fault detection and multi-classifier fusion for unmanned aer...

引用

conference on sensor fusion - architectures, algorithms, and applications V

作者： Yan, WZ GE Corp R&D Niskayuna NY 12309 USA

ISBN: (纸本)0819440809

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a piloted aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naive Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers') show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

关键词： fault detection classifier fusion classifier combination UAVs

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Optimal interval estimation fusion based on sensor interval estimates and confidence degrees

Optimal interval estimation fusion based on sensor interval ...

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

作者： Zhu, YM Li, BH Sichuan Univ Dept Math Chengdu 610064 Peoples R China

ISBN: (纸本)0819449598

The interval estimation fusion method based on sensor interval estimates and their confidence degrees is developed. When sensor estimates are independent of each other, a combination rule to merge sensor estimates and their confidence degrees is proposed. Moreover, two popular optimization-criteria: minimizing interval length with an allowable minimum confidence degree, or maximizing confidence degree with an allowable maximum interval length are suggested. In terms of the two criteria, an optimal interval estimation fusion can be obtained based on the combined intervals and their confidence degrees. Then we can extend the results on the combined interval outputs and. their confidence degrees to obtain a conditional combination rule and the corresponding optimal fault-tolerant interval estimation fusion in terms of the-two criteria. It is easy to see that Marzullo's fault-tolerant interval estimation fusion(11) is a special case of our method. We also point out that in some sense, our combination rule is similar to the combination rule in Dempster-Shafer evidence theory. However, the confidence degrees given in this paper is summable, but they (called mass function in Dempster-Shafer evidence theory) are not there;therefore, Dempster-Shafer's combination rule is not applicable to the interval estimation fusion.

关键词： interval estimation confidence degree combination ride optimal interval estimation fusion

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Integrated spatio-temporal multiple sensor fusion system design

Integrated spatio-temporal multiple sensor fusion system des...

引用

conference on sensor fusion - architectures, algorithms, and applications VI

作者： Chen, HW Olson, T Lockheed Martin Missiles & Fire Control Orlando Orlando FL 32819 USA

ISBN: (纸本)0819444812

In many missile and fire control applications, targets of interest may be acquired and tracked over some finite period of time with one or more sensors. This allows for the collection of sequential segments or frames of temporal information per sensor as well as across various sensors. By appropriately processing this information, target detection and classification performance can be considerably increased. Furthermore, we have developed new and different fusion strategies (additive and MINMAX fusion) in addition to the traditional strategies. Our test and analysis results show that temporal fusion can improve target classification as well as spatial fusion. In this work we have developed an optimal and novel design for an integrated spatio-temporal multi-sensor fusion system that combines inputs from different sensors as well as from the different time frames of each sensor.

关键词： sensors Signal to noise ratio sensor fusion Reliability Infrared sensors Missiles Statistical analysis Target detection Analytical research Fuzzy logic

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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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Multi-agent data fusion workstation (MADFW) architecture

Multi-agent data fusion workstation (MADFW) architecture

引用

conference on sensor fusion - architectures, algorithms, and applications II

作者： Shahbazian, E Bosse, E Valin, P Lockheed Martin Canada Inc Montreal PQ H4P 1K6 Canada

ISBN: (纸本)0819428256

This paper describes an on-going effort to build a Multi-Agent Data fusion Workstation (MADFW) based on a Knowledge-Based System (KBS) BlackBoard (BB) architecture to offer a range of innovative techniques for Data fusion (DP), applicable to various domains. The initial application to be demonstrated is in the area of airborne maritime surveillance where several multi-agent concepts and algorithms have already been studied and demonstrated. The end result will offer the user a flexible and modular environment providing capability for: (a) addition of user defined sensor simulation models and fusion algorithms;(b) integration with existing models and algorithms;and (c) evaluation of performance to derive requirement specifications and help in the design phase towards fielding a real DF system. The workstation is being designed to accommodate modular interchangeable algorithm implementation and performance evaluation of: (1) fusion of positional data from imaging and non-imaging sensors;(2) fusion of attribute information obtained from imaging and non-imaging sensors and other sources such as communication systems, satellites, etc.;and (3) Object Recognition (OR) in imaging dal-a. The design allows algorithms for sensor simulators and measures of performance to reside either on the KBS BE shell or be separate from it, thus facilitating integration with other testbed designs. This architecture also allows the future introduction of fusion management capabilities. The real-time KBS BE shell developed by Lockheed Martin Canada, in collaboration with DREV, is the basis of the MADFW infrastructure. This system is totally generic, and could be used to implement any system comprising of components which can be numeric or AI based. It has been implemented in C++ rather than in a higher-level language (such as LISP, Smalltalk,...) to satisfy the real-time requirement.

关键词： intelligent decision aids data fusion image analysis automatic target recognition knowledge-based system blackboard

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Beyond third generation: A sensor fusion targeting FLIR pod for the F/A-18

Beyond third generation: A sensor fusion targeting FLIR pod ...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Krebs, WK Scribner, DA Miller, GM Ogawa, JS Schuler, J USN Postgrad Sch Dept Operat Res Monterey CA 93950 USA

ISBN: (纸本)0819428256

The Navy and Marine Corps F/A-18 pilots state that the targeting FLIR system does not provide enough target definition and clarity. As a result, high altitude tactics missions are the most difficult due to the limited amount of time available to identify the target. If the targeting FLIR system had a better standoff range and an improved target contrast then the pilots' task would be easier. Unfortunately, the replacement cost of the existing FLIR equipment is prohibitive. The purpose of this study is to modify the existing F/A-18 targeting FLIR system with a dual-band color sensor to improve target contrast and stand-off ranges. Methods: A non-real-time color sensor fusion system was flown on a NASA F/A-18 in a NITE Hawk targeting FLIR pod. Flight videotape was recorded from a third generation image intensified CCD and a first generation long-wave infrared sensor. A standard visual search task was used to assess whether pilots' situational awareness was improved by combining the two sensor videotape sequences into a single fused color or grayscale representation, Results: Fleet aviators showed that color fusion improved target detection, but hindered situational awareness. Aviators reported the lack of color constancy caused the scene to be unaesthetically pleasing;however, target detection was enhanced. Conclusion: A color fusion scene may benefit targeting applications but hinder situational awareness.

关键词： sensor fusion electro-optics targeting human performance FLIR image enhancement

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Surface texture measurement by combining signals from two sensing elements of a piezoelectric tactile sensor

Surface texture measurement by combining signals from two se...

引用

conference on sensor fusion - architectures, algorithms, and applications II

作者： Dargahi, J Payandeh, S Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)0819428256

In this paper we report on the design and testing of a prototype surface texture tactile sensor. The sensor can measure both compliance and surface roughness. The design of the sensor is based on the psychophysiological perception of surface texture by the human hand. The sensor essentially consists of a rigid cylinder surrounded by a compliant cylinder. The deformation of the compliant object from rigid to compliant cylinder is used for measuring the compliance of the contact object and variation of the compliant cylinder over a surface profile with reference to the rigid cylinder is used to measurement surface roughness. Two 25 mu m thick polyvinylidene fluride(PVDF) films are used as a tranducer in the tactile sensor system. The sensor in miniaturized form can be used in a laparoscopic grasper for minimally invasive surgery. The theoretical analysis is made and compared with experimental values. The advantages and limitations of the sensor are also discussed.

关键词： piezoelectric robotic tactile sensor laparoscope polyvinylinede fluoride minimally invasive surgery

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Conceptual Exploration Package for Data fusion

Conceptual Exploration Package for Data Fusion

引用

sensor fusion: architectures, algorithms, and applications iv

作者： Jousselme, A.L. Grenier, D. Bosse, E. Universite Laval Ste-Foy Canada

In this paper, we present a software package designed to explore data fusion area applied to different contexts. This tool, called CEPfuse (Conceptual Exploration Package for Data fusion) provides a good support to become familiar with all concepts and vocabulary linked to data fusion. Developed with Matlab 5.2, it's also a good tool to test, compare and analyze algorithms. Although the core of this package is evidential reasoning and identity information fusion, it has been conceived to develop all the interesting part of the Multi-sensor Data fusion (MSDF) system. Actually, because we concentrate our research work on identity information fusion, the principal included algorithms are Dempster-Shafer rules of combination. Shafer-Logan algorithms for hierarchical structures, and several decision rules.

关键词： sensor data fusion

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Neurophysiological-based signals analysis for level 5 user refinement for target localization

Neurophysiological-based signals analysis for level 5 user r...

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conference on sensor fusion - architectures, algorithms, and applications VI

作者： Blasch, EP Wright State Univ Dayton OH 45435 USA

ISBN: (纸本)0819444812

The human brain fuses information from a variety of modalities to locate, track, and identify targets. Vision-based tracking, which uses a 2D signal, is able to accurately identify and locate objects, however it requires more processing time than 1-D auditory systems. Auditory systems can locate and identify objects based on interaural time difference (ITD) and interaural intensity difference (IID) fusion. In order to investigate the advantages of a neurophysiology-based fusion model, we seek to localize a target from a 1-D signals analysis conducted over repeated measurements where a user is allowed move sensors. Similar to the human fusing auditory information from the ears, we seek to fuse information over time and space from two sensors monitoring a single target. Through spatiotemporal fusion from the 1-D analysis, we show how ITD and IID fusion functions support a Level 5 User Refinement task.

关键词： information fusion auditory system ITD IID tracking identification ATR

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