Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 307
Book Description
The aim of this AGARDograph is to provide a quick overview of practical advances in multi-sensor, multi-target tracking technology and applications. This will provide the general summary of the MS/MTT techniques and technology with emphasis towards practical implementation. Many examples of sensor fusion involve the methodology of merging various track files taken from different sensors. This allows for more consistent, accurate, and reliable tracks than might be possible with any of the individual systems acting alone. Section 1 relates to the important use of sensor fusion prior to establishing a firm track file. By combining raw sensor information, greater discrimination of targets from background may be possible from the augmented body of available information. Tracking and fusion with multiple sensors deals with integration and correlation of data from diverse sources in order to arrive at the best possible situational assessment. In Section II, we present the tutorial on representative data association and filtering techniques, and also address some of the key initiation issues, approaches and track management methodology that simplify and enhance the practical implementation. Section III presents different types of classification algorithms, Bayesian Belief Networks, and Neural Networks covering the complete Automatic Target Recognition process, including fusion, segmentation and classification, that are very promising for real time, or quasi real time systems applications. Section IV covers the handling of Automatic Target Recognition (ATR) test data, deals with an effective tool to support the development of precision guided munitions, and presents a study of target acquisition and sensor cueing in air to air environment, The last section presents several practical examples of MS/MTT applications.
Multi-Sensor Multi-Target Data Fusion, Tracking and Identification Techniques for Guidance and Control Applications (Les Techniques de Poursuite Et D'identification Multi-cibles a Base de Fusion Multi-senseur Appliquees Au Guidage Et Au Pilotage).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 307
Book Description
The aim of this AGARDograph is to provide a quick overview of practical advances in multi-sensor, multi-target tracking technology and applications. This will provide the general summary of the MS/MTT techniques and technology with emphasis towards practical implementation. Many examples of sensor fusion involve the methodology of merging various track files taken from different sensors. This allows for more consistent, accurate, and reliable tracks than might be possible with any of the individual systems acting alone. Section 1 relates to the important use of sensor fusion prior to establishing a firm track file. By combining raw sensor information, greater discrimination of targets from background may be possible from the augmented body of available information. Tracking and fusion with multiple sensors deals with integration and correlation of data from diverse sources in order to arrive at the best possible situational assessment. In Section II, we present the tutorial on representative data association and filtering techniques, and also address some of the key initiation issues, approaches and track management methodology that simplify and enhance the practical implementation. Section III presents different types of classification algorithms, Bayesian Belief Networks, and Neural Networks covering the complete Automatic Target Recognition process, including fusion, segmentation and classification, that are very promising for real time, or quasi real time systems applications. Section IV covers the handling of Automatic Target Recognition (ATR) test data, deals with an effective tool to support the development of precision guided munitions, and presents a study of target acquisition and sensor cueing in air to air environment, The last section presents several practical examples of MS/MTT applications.
Publisher:
ISBN:
Category :
Languages : en
Pages : 307
Book Description
The aim of this AGARDograph is to provide a quick overview of practical advances in multi-sensor, multi-target tracking technology and applications. This will provide the general summary of the MS/MTT techniques and technology with emphasis towards practical implementation. Many examples of sensor fusion involve the methodology of merging various track files taken from different sensors. This allows for more consistent, accurate, and reliable tracks than might be possible with any of the individual systems acting alone. Section 1 relates to the important use of sensor fusion prior to establishing a firm track file. By combining raw sensor information, greater discrimination of targets from background may be possible from the augmented body of available information. Tracking and fusion with multiple sensors deals with integration and correlation of data from diverse sources in order to arrive at the best possible situational assessment. In Section II, we present the tutorial on representative data association and filtering techniques, and also address some of the key initiation issues, approaches and track management methodology that simplify and enhance the practical implementation. Section III presents different types of classification algorithms, Bayesian Belief Networks, and Neural Networks covering the complete Automatic Target Recognition process, including fusion, segmentation and classification, that are very promising for real time, or quasi real time systems applications. Section IV covers the handling of Automatic Target Recognition (ATR) test data, deals with an effective tool to support the development of precision guided munitions, and presents a study of target acquisition and sensor cueing in air to air environment, The last section presents several practical examples of MS/MTT applications.
Multi-sensor Multi-target Data Fusion, Tracking and Identification Techniques for Guidance and Control Applications
Author:
Publisher:
ISBN:
Category : Guided missiles
Languages : en
Pages : 312
Book Description
Resumé på fransk.
Publisher:
ISBN:
Category : Guided missiles
Languages : en
Pages : 312
Book Description
Resumé på fransk.
MULTI-SENSOR MULTI-TARGET DATA FUSION, TRACKING AND IDENTIFICATION TECHNIQUES FOR GUIDANCE AND CONTROL APPLICATIONS.
Author: North Atlantic Treaty Organization
Publisher:
ISBN:
Category :
Languages : en
Pages : 295
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 295
Book Description
Multi-sensor Multi-target Data Fusion, Tracking and Indentification Techniques for Guidance and Control Applications
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set
Author: Yuming Gong
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 18
Book Description
To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source information can improve the accuracy of target recognition. However, the recognition capabilities of different sensors are different during target recognition, and the complementarity between sensors needs to be analyzed during information fusion. This paper proposes a multi-sensor fusion recognition method based on complementarity analysis and neutrosophic set. The proposed method mainly has two parts: complementarity analysis and data fusion. Complementarity analysis applies the trained multi-sensor to extract the features of the verification set into the sensor, and obtain the recognition result of the verification set. Based on recognition result, the multi-sensor complementarity vector is obtained. Then the sensor output the recognition probability and the complementarity vector are used to generate multiple neutrosophic sets. Next, the generated neutrosophic sets are merged within the group through the simplified neutrosophic weighted average (SNWA) operator. Finally, the neutrosophic set is converted into crisp number, and the maximum value is the recognition result. The practicality and effectiveness of the proposed method in this paper are demonstrated through examples.
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 18
Book Description
To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source information can improve the accuracy of target recognition. However, the recognition capabilities of different sensors are different during target recognition, and the complementarity between sensors needs to be analyzed during information fusion. This paper proposes a multi-sensor fusion recognition method based on complementarity analysis and neutrosophic set. The proposed method mainly has two parts: complementarity analysis and data fusion. Complementarity analysis applies the trained multi-sensor to extract the features of the verification set into the sensor, and obtain the recognition result of the verification set. Based on recognition result, the multi-sensor complementarity vector is obtained. Then the sensor output the recognition probability and the complementarity vector are used to generate multiple neutrosophic sets. Next, the generated neutrosophic sets are merged within the group through the simplified neutrosophic weighted average (SNWA) operator. Finally, the neutrosophic set is converted into crisp number, and the maximum value is the recognition result. The practicality and effectiveness of the proposed method in this paper are demonstrated through examples.
Multisensor Data Fusion
Author: David Hall
Publisher: CRC Press
ISBN: 9781420038545
Category : Technology & Engineering
Languages : en
Pages : 586
Book Description
The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut
Publisher: CRC Press
ISBN: 9781420038545
Category : Technology & Engineering
Languages : en
Pages : 586
Book Description
The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut
Investigations on Target Tracking and Classification Using Multiple Sensor Data Fusion, Task II:
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 231
Book Description
The goal of this report is to conduct an exhaustive formal literature survey on multisensor data fusion and use the results to conduct performance analyses of the following sensor data fusion subjects: sensor data association & fusion architectures; data association; data fusion; data alignment or registration; target attribute estimation & fusion; application of artificial intelligence techniques; target state estimation analysis; and target model (type of maneuver) identification analysis. The different analysis approaches published in the surveyed literature are identified for each of the above subjects, and relative merits and trade-offs between these approaches are evaluated. The analyses focus on those approaches which could be pertinent to a naval platform employing dissimilar and non-imaging sensors. Includes glossary.
Publisher:
ISBN:
Category :
Languages : en
Pages : 231
Book Description
The goal of this report is to conduct an exhaustive formal literature survey on multisensor data fusion and use the results to conduct performance analyses of the following sensor data fusion subjects: sensor data association & fusion architectures; data association; data fusion; data alignment or registration; target attribute estimation & fusion; application of artificial intelligence techniques; target state estimation analysis; and target model (type of maneuver) identification analysis. The different analysis approaches published in the surveyed literature are identified for each of the above subjects, and relative merits and trade-offs between these approaches are evaluated. The analyses focus on those approaches which could be pertinent to a naval platform employing dissimilar and non-imaging sensors. Includes glossary.
Multitarget/multisensor Data Fusion Techniques for Detection, Identification, and Tracking
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Multitarget-multisensor Tracking
Author: Yaakov Bar-Shalom
Publisher:
ISBN: 9780964831209
Category : Radar
Languages : en
Pages : 615
Book Description
Publisher:
ISBN: 9780964831209
Category : Radar
Languages : en
Pages : 615
Book Description
Multitarget-multisensor Tracking: Applications and advances
Author: Yaakov Bar-Shalom
Publisher:
ISBN:
Category : Radar
Languages : en
Pages : 474
Book Description
Publisher:
ISBN:
Category : Radar
Languages : en
Pages : 474
Book Description