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检索条件"任意字段=Conference on Signal Processing, Sensor Fusion, and Target Recognition XX"

共 426 条记录，以下是1-10 订阅

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Interactive target recognition in images using machine-learning techniques

Interactive target recognition in images using machine-learn...

引用

conference on signal processing, sensor fusion, and target recognition xx

作者： Michaeli, Ariel Camon, Irit RAFAEL Advanced Defense Systems Ltd. (Israel)

ISBN: (纸本)9780819486240

Imagery analysis systems utilize Automatic target recognition (ATR) methods in order to improve the accuracy of human-based analysis and save time. Often, ATR methods perform poorly in obtaining these objectives, due to reliance on outdated prior information, while human operators possess updated information that remains unused. This paper presents an interactive target recognition (or ITR) application. The operator marks sample target pixels by an intuitive user-interface. Then machine-learning techniques generate algorithms tailored for their recognition in imagery. The resulting detection map is dynamically controlled by the operator, suiting his needs. The application enables target recognition in zero prior information environments.

关键词： ATR Interactive target recognition Machine Learning User interface sensor fusion

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Dempster's combination is a special case of Bayes' rule

Dempster's combination is a special case of Bayes' rule

引用

conference on signal processing, sensor fusion, and target recognition xx

作者： Mahler, Ronald Lockheed Martin MS2 Eagan MN USA

ISBN: (纸本)9780819486240

Bayes' rule and Dempster's combination are typically presumed to be radically different procedures for fusing evidence. This paper demonstrates that measurement-update using Dempster's combination is a special case of measurement-update using Bayes' rule. The demonstration is based on an analogy with the Kalman filter. Suppose that the data consists of linear-Gaussian point measurements. Then ask, What additional assumptions must be made so that the Bayes filter can be solved in algebraically closed form? The Kalman filter is the result. In similar fashion, suppose that the data consists of measurements that are "uncertain" in a Dempster-Shafer sense. Then ask, What additional assumptions must be made so that the Bayes filter can be solved in algebraically closed form? Dempster's combination turns out to be the result. Stated differently: Both the Kalman measurement-update equations and Dempster's combination are corrector steps of the recursive Bayes filter, given that it has been restricted to two different types of measurements.

关键词： Filtering (signal processing) Digital filtering Fuzzy logic Electronic filtering target recognition Current controlled current source Error analysis sensor fusion sensors signal processing

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Real-Time sensor fusion technique for Acoustic and Seismic sensors

Real-Time Sensor Fusion technique for Acoustic and Seismic S...

引用

conference on signal processing, sensor fusion, and target recognition xx

作者： Zubair, Mussab Hartmann, Klaus Loffeld, Otmar Univ Siegen Ctr SensorSyst ZESS D-57068 Siegen Germany

ISBN: (纸本)9780819486240

Spatially distributed network of sensor nodes with onboard seismic and acoustic sensors is an important class of emerging networked systems for various security applications. One of a main task in these applications is target detection. In order to achieve this with improved accuracy, it is necessary that sensors process and share information efficiently. This paper presents a novel approach to fuse the data of acoustic and seismic sensors based on correlation measures so that high detection range and/or detection rate can be achieved. This method calculates the weighted values of both the sensors and the values of their weights are adjusted as the change of correlation measures is observed. It gives greater weighted value to the greater correlation measure of the sensor signals, and vice versa. One of the advantages of this method is that the weights of the sensors are adjusted dynamically for real-time data. The method does not depend on prior information of the sensor's data. This method considers the limited range of both the acoustic and the seismic sensors and fuses the signals in terms of maximum possible detection range. In case of failure of one of the sensor, the method still provides the target information.

关键词： Seismic Acoustic sensor fusion Correlation sensor Network

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sensor fusion FOR target recognition - A REVIEW OF FUNDAMENTALS AND A POTENTIAL APPROACH TO MULTIsensor REQUIREMENTS ALLOCATION

SENSOR FUSION FOR TARGET RECOGNITION - A REVIEW OF FUNDAMENT...

引用

20th Infrared Technology conference

作者： HARNEY, RC Orlando FL USA

ISBN: (纸本)0819415936

In the first part of this paper, a brief tutorial review of sensor fusion for target recognition applications is presented. In this context, relevant aspects of system architecture, sensor integration, and data fusion are discussed. Several unresolved issues in the practical implementation of sensor fusion are identified; significant among these are the rationale for selection of a sensor suite and a means for optimal allocation of requirements between sensors. In the second part, a novel methodology offering the potential for resolving these two issues is presented. An approach to defining sensor data information content is described. Coupling this approach with accepted rules of thumb describing target recognition capabilities results in a quantitative method for comparing the target recognition ability of diverse sensors (imagers, non-imagers, active, passive, electromagnetic, acoustic, etc.). Extension to describing the performance of multiple sensors is straightforward. Application of the technique to sensor selection and requirements allocation is discussed.

关键词： sensors target recognition LIDAR Image processing Infrared technology signal to noise ratio Spatial resolution sensor fusion Thermography Image sensors

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Fundamentals of the Dempster-Shafer Theory

Fundamentals of the Dempster-Shafer Theory

引用

conference on signal processing, sensor fusion, and target recognition xxI

作者： Peri, Joseph S. J. Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA

ISBN: (纸本)9780819490704

In this paper, I discuss the basic notions of the Dempster Shafer theory. Using a simple engineering example, I highlight sources of confusion in the Dempster Shafer literature, and some questions that arise in the course of applying the Dempster Shafer algorithm Finally, I discuss the measure theoretic foundation that reveals the intimate connections between the Dempster Shafer theory and Probability theory.

关键词： Probability theory sensors Algorithms Applied physics Current controlled current source Head Mathematics sensor fusion signal processing target recognition

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Bayesian cluster detection and tracking using a generalized Cheeseman approach

Bayesian cluster detection and tracking using a generalized ...

引用

conference on signal processing, sensor fusion, and target recognition XII

作者： Mahler, R Lockheed Martin Tactical Syst Eagan MN USA

ISBN: (纸本)0819449563

Cluster tracking is the problem of detecting and tracking clustered formations of large numbers of targets, without necessarily being obligated to track each and every individual target. We address this problem by generalizing to the dynamic case a static Bayesian finite-mixture data-clustering approach due to P. Cheeseman. After summarizing Cheeseman's approach, we show that it implicitly draws on random set theory. Making this connection explicit allows us to incorporate it into a multitarget recursive Bayes filter, thereby leading to a rigorous Bayesian foundation for finite-mixture cluster tracking. A computational approach is proposed, based on an approximate, multitarget first-order moment filter ("cluster PHD" filter).

关键词： Digital filtering target detection Electronic filtering Filtering (signal processing) Data modeling Statistical modeling Detection and tracking algorithms sensor fusion signal processing target recognition

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Hierarchical multifeature integration for automatic target recognition

Hierarchical multifeature integration for automatic target r...

引用

conference on signal processing, sensor fusion, and target recognition VIII

作者： Shah, S Aggarwal, JK Wayne State Univ Comp Vis Lab Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)081943194X

This paper presents a methodology for object recognition in complex scenes by learning multiple feature object representations in second generation Forward Looking InfraRed (FLIR) images. A hierarchical recognition framework is developed which solves the recognition task by performing classification using decisions at the lower levels and the input features. The system uses new algorithms for detection and segmentation of objects and a Bayesian formulation for combining multiple object features for improved discrimination. Experimental results on a large database of FLIR images is presented to validate the robustness of the system, and its applicability to FLIR imagery obtained from real scenes.

关键词： target recognition sensor fusion Bayesian classification

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fusion-based methods for target identification in the absence of quantitative classifier confidence

Fusion-based methods for target identification in the absenc...

引用

conference on signal processing, sensor fusion, and target recognition VI

作者： Llinas, J SUNY/Buffalo (United States)

ISBN: (纸本)0819424838

In an era of reduced defense budgets, there is increased pressure to reuse any available technology or capability to the extent possible. For data fusion applications, this requirement can lead to situations where the output of disparate individual algorithms would like to be fused;ideally, this would be done in the most quantitative way possible. This paper reviews, integrates, and comments on various prior works in both the data fusion, remote sensing, and character recognition communities which are helpful to the data fusion algorithm/process designer dealing, in particular, with target identification and classification problems. It is shown that generalized voting and rank-based methods may be useful in these cases;the issue of source reliability is also addressed and methods for incorporating assigned reliabilities are described.

关键词： data fusion sensor fusion target identification target classification ranking methods voting methods

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Tactical Multi sensor fusion (TMSF) an SAIC white paper

Tactical Multi Sensor Fusion (TMSF) an SAIC white paper

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conference on signal processing, sensor fusion, and target recognition VIII

作者： Henderson, B Short, B Stewart, C SAIC Sterling VA 20164 USA

ISBN: (纸本)081943194X

A software application for correlating and fusing information products from multiple dissimilar sensors is presented. The Tactical Multi sensor fusion (TMSF) system is a C++ object oriented application implementing data correlation and fusion algorithms which provide decision aids for identifying, locating, and determining the status of processing equipment within suspected weapons of mass destruction (WMD) sites. The TMSF system also provides valuable information for assessing weapon delivery accuracy and effectiveness.

关键词： sensors Data fusion Weapons of mass destruction Data modeling Error analysis Interfaces Weapons Image fusion sensor fusion Data processing

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Performance analysis of an asynchronous track fusion and architecture

Performance analysis of an asynchronous track fusion and arc...

引用

conference on signal processing, sensor fusion, and target recognition VI

作者： Alouani, AT Rice, TR Tennessee Technological Univ. (United States) Naval Surface Warfare Ctr. (United States)

ISBN: (纸本)0819424838

In practice, Multisensor systems use dissimilar sensors having different data rates. Such sensors may also have inherent delays as well as communication delays. Recently the authors developed a track fusion algorithm that attempted to account for realistic constraints of sensor fusion. The objective of this paper are two folds. First, it shows that the synchronous track fusion problem can be derived as a special case of the developed track fusion algorithm. Second, using simulated target tracks, the performance of the asynchronous track fusion (ASTF) algorithm is analyzed and compared to an existing fusion algorithm. Different sensor data rates and communication delays are used in the simulations.

关键词： sensors Algorithm development Detection and tracking algorithms Data communications Monte Carlo methods Computer simulations Data processing Information technology Lithium sensor fusion

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