6 edition of Multisensor Decision and Estimation Fusion found in the catalog.
November 30, 2002
Written in English
|Series||The International Series on Asian Studies in Computer and Information Science|
|The Physical Object|
|Number of Pages||264|
Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and Cited by: A comparison of criteria for decision fusion and parameter estimation in statistical multisensor image classification Abstract: We study two related topics in decision fusion for multisensor image classification. The first topic is the use of a weighted logarithmic opinion pool compared to the statistical product combination by: 2.
Multi-sensor data fusion of DCM based orientation estimation for land vehicles Abstract: In this paper, an algorithm estimating orientation is implemented using Direction Cosine Matrix (DCM) method, chosen due to its linear process model and ease of use. Two Kalman filters were used to estimate the rotation matrix elements where the Euler Cited by: Exploring recent signficant results, this book presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. It covers general adapted methods and systematic results, includes computer experiments to support the theoretical results, and fixes several popular but incorrect results in the field"
Abstract. This paper presents a significant integrated optimization point of view behind the following three successful decision and estimation fusion results: 1) a unified fusion rule for networked sensor decision systems; 2) optimal sensor data quantization for estimation fusion and 3) integrated multi-target data association tracking by: 4. Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world's leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art.
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This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the : Paperback. YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate Brand: Springer US.
Multisensor Decision And Estimation Fusion (The International Series on Asian Studies in Computer and Information Science Book 14) - Kindle edition by Yunmin Zhu. Download it once and read it on your Kindle device, PC, phones or tablets.
Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in Cited by: Buy Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods: Multisensor Decision and Estimation Fusion book Books Reviews - : Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods eBook: Yunmin Zhu, Jie Zhou, Xiaojing Shen, Enbin Song, Yingting Luo: Kindle StorePrice: $ Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods by Yunmin Zhu () on *FREE* shipping on qualifying offers.
Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods by. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods - CRC Press Book Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areas—including sensor networks, space technology, air traffic control, military.
This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors.
Networked Multisensor Decision and Estimation Fusion. Networked Multisensor Decision and Estimation Fusion book.
Based on Advanced Mathematical Methods. By Yunmin Zhu, Jie Zhou, Xiaojing Shen, Enbin Song, Yingting Luo.
Edition 1st Edition. First Published eBook Published 5 July Cited by: Multisensor Decision And Estimation Fusion. [Yunmin Zhu] -- YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed.
Cite this chapter as: Zhu Y. () Multisensor Point Estimation Fusion. In: Multisensor Decision And Estimation Fusion. The International Series on Asian Studies Author: Yunmin Zhu.
Provides a treatment of the fundamentals of multisensor decision and estimation fusion in order to deal with general random observations or observation noises that are correlated across the sensors. For the multisensor point estimation fusion problem, a general version of the linear unbiased minimum variance estimation fusion rule is developed.
Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods | Zhu, Yunmin (Sichuan University, Chengdu, China), Zhou, Jie (Sichuan University, China), Shen, Xiaojing (Sichuan University, China), Song, Enbin (Sichuan University, PR of China), Luo, Yingting (Sichuan University, PR of China) | ISBN: | Kostenloser Versand für alle Bücher mit Versand Format: Gebundenes Buch.
Estimation with Multisensor Fusion. The estimation/decision algorithm presented here can be used for an aircraft to decide, in a timely manner, whether appropriate countermeasures are : Yaakov Bar-Shalom.
In this chapter, we consider the multisensor interval estimation fusion which is different from the point estimation fusion. In the point estimation problems, a popular criterion is to minimize the distance between the estimate and the true value under a proper metric, such as the minimum variance : Yunmin Zhu.
Handbook of multisensor data fusion [Book Review] One yearns for a handbook of multisensor data fusion The choice between Bayesian and Dempster-Shafer inference methods for decision Author: Fred Daum. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods View larger image By: Jie Zhou and Yunmin Zhu and Xiaojing Shen and Enbin Song and Yingting Luo.
Introduction to multi-sensor data fusion Conference Paper (PDF Available) in Proceedings - IEEE International Symposium on Circuits and Systems - vol.6 June with 2, Reads.
The book examines the underlying principles of sensor operation and data fusion, the techniques and technologies that enable the process, including the operation of 'fusion engines'.
Fundamental theory and the enabling technologies of data fusion are presented in a systematic and accessible manner. Intrusion Detection Systems and Multisensor Data Fusion. chastic models for noise and false alarm estimation .
are required in the decision-level identity fusion. process. (For more. Integrated optimization methods in multisensor decision and estimation fusion Article in Sciece China. Information Sciences 55(3) March with 10 Reads. 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.The journal is intended to present within a single forum all of the developments in the field of multi-sensor, multi-source, multi-process information fusion and thereby promote the synergism among the many disciplines that are contributing to its growth.
The journal is the premier vehicle for disseminating information on all aspects of.