multisensor decision estimation fusion di zhu yunmin (25 risultati)

hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Hardcover. Condizione: Fine. Kluwer Academic hardcover, 2003, unused/unmarked, some surface rub to glossy cover otherwise as New. We will bubble-wrap the book and ship it in a BOX with delivery confirmation/tracking. "Multisensor Decision And Estimation Fusion" by Zhu (as pictured).

Condizione: New. pp. 264.

Condizione: New. pp. 264 Illus.

Condizione: New. pp. 264.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New. SUPER FAST SHIPPING.

Hardcover. Condizione: Like New. Like New. book.

Condizione: New.

Condizione: New. In.

Gebunden. Condizione: New.

Condizione: New.

Taschenbuch. Condizione: Neu. Multisensor Decision And Estimation Fusion | Yunmin Zhu | Taschenbuch | xxi | Englisch | 2012 | Springer US | EAN 9781461353676 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Condizione: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch.

Taschenbuch. Condizione: Neu. 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.

Buch. Condizione: Neu. 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.

Hardcover. Condizione: Like New. Like New. book.

Paperback. Condizione: Like New. Like New. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 264 pp. Englisch.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 264 pp. Englisch.

Buch. Condizione: Neu. Multisensor Decision And Estimation Fusion | Yunmin Zhu | Buch | xxi | Englisch | 2002 | Springer | EAN 9781402072581 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch.