Druck auf Anfrage Neuware - Printed after ordering - 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 is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. 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.

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Bibliographic Details

Title: Multisensor Decision And Estimation Fusion
Publisher: Springer US, Springer New York
Publication Date: 2002
Language: English
ISBN 10: 1402072589
ISBN 13: 9781402072581
Binding: Buch
Condition: Neu
Dimensions: 241 millimeters width by 160 millimeters height by 19 millimeters depth
Weight: 565 grams

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Synopsis

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 is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob� lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec� tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. 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.

Synopsis

In recent years, research activity in multisensor decision and estimation fusion problems has significantly increased. Distributed decision and estimation fusion problems for cases with statistically independent observations - or observation noises - have received the most attention, while problems with statistically dependent observations have been given much less consideration. "Multisensor Decision and Estimation Fusion" provides a more complete 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. Recent progress is presented in two ways. For multisensor decision fusion with general sensor observations given a fixed fusion rule, the book demonstrates a necessary condition for optimum sensor rules - they must be a fixed point of an integral operator related to given conditional probability densities. In particular, the book presents unified/optimal fusion rules for some specific decision systems. For the multisensor point estimation fusion problem, a general version of the linear unbiased minimum variance estimation fusion rule is developed.

In addition, several new interval estimation fusion methods are proposed.

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