Results 31 to 40 of about 69 (69)
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The 22nd IEEE Conference on Decision and Control, 1983
Consider the following estimation problem: The state trajectory of a random process is observed by K distinct observers, using noise-corrupted observations. Each observer processes his own observation history, to obtain the local conditional distribution of the state, as a function of time.
David A. Castanon +1 more
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Consider the following estimation problem: The state trajectory of a random process is observed by K distinct observers, using noise-corrupted observations. Each observer processes his own observation history, to obtain the local conditional distribution of the state, as a function of time.
David A. Castanon +1 more
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, 2015 A numerical method for solving a class of constrained minimization problems encountered in quaternion data fusion is presented. The quaternion constraints are handled by the method of Lagrange multipliers. The number of the stationary points of the minimization problem is finite and all of them are found by solving via homotopy continuation a system of
William D. Banas +2 more
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Proceedings of 26th Southeastern Symposium on System Theory, 2002
In a multisensor tracking system, sensors often operate asynchronously and provide data at different rates with different communication delays. In this case the sequential processing of the sensor data may be computationally intensive. In addition, due to the inherent delay associated with some sensors, such as a multi-tasking radar, it may not be ...
Theodore R. Rice, Ali T. Alouani
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In a multisensor tracking system, sensors often operate asynchronously and provide data at different rates with different communication delays. In this case the sequential processing of the sensor data may be computationally intensive. In addition, due to the inherent delay associated with some sensors, such as a multi-tasking radar, it may not be ...
Theodore R. Rice, Ali T. Alouani
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IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120), 2002
The study of the relations between environmental changes and socioeconomic situations is of great relevance to the definition of the impact that national policies have on the environment. In order to understand such relations the authors have taken into consideration those changes taking place on a provincial scale in the period going from 1975 to 1992.
E. Console +3 more
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The study of the relations between environmental changes and socioeconomic situations is of great relevance to the definition of the impact that national policies have on the environment. In order to understand such relations the authors have taken into consideration those changes taking place on a provincial scale in the period going from 1975 to 1992.
E. Console +3 more
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Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics, 2002
An adaptive algorithm for multi-sensor/data fusion is developed. The algorithm can be employed in an uncertain sensed environment using imperfect sensors. Assuming little prior information about the sensed environment and the sensors, the algorithm adaptively adjusts the weights for the best fusion of inaccurate information provided by the multiple ...
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An adaptive algorithm for multi-sensor/data fusion is developed. The algorithm can be employed in an uncertain sensed environment using imperfect sensors. Assuming little prior information about the sensed environment and the sensors, the algorithm adaptively adjusts the weights for the best fusion of inaccurate information provided by the multiple ...
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The International Journal of Robotics Research, 1988
This paper treats two parts of the methodology of the fusion of multisensor data: conceptual and applied. In the concep tual part, we treat several basic questions including the fu sionability of various kinds of signals derived from the differ ent sets of raw data associated with separate sensor systems.
Kenneth A. Marsh, John M. Richardson
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This paper treats two parts of the methodology of the fusion of multisensor data: conceptual and applied. In the concep tual part, we treat several basic questions including the fu sionability of various kinds of signals derived from the differ ent sets of raw data associated with separate sensor systems.
Kenneth A. Marsh, John M. Richardson
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1998
The main objective of multisensor data fusion is to combine elements of raw data from multiple sensors to extract the greatest amount of information possible about the sensed environment which is greater than the sum of its contributing parts. In the literal sense, data refers to the actual measurements taken or information obtained by the sensors and ...
Erdal Panayirci +3 more
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The main objective of multisensor data fusion is to combine elements of raw data from multiple sensors to extract the greatest amount of information possible about the sensed environment which is greater than the sum of its contributing parts. In the literal sense, data refers to the actual measurements taken or information obtained by the sensors and ...
Erdal Panayirci +3 more
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Journal of Intelligent & Robotic Systems, 1988
This paper reviews some knowledge representation approaches devoted to the sensor fusion problem, as encountered whenever images, signals, text must be combined to provide the input to a controller or to an inference procedure. The basic steps involved in the derivation of the knowledge representation scheme, are: (A) locate a representation ...
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This paper reviews some knowledge representation approaches devoted to the sensor fusion problem, as encountered whenever images, signals, text must be combined to provide the input to a controller or to an inference procedure. The basic steps involved in the derivation of the knowledge representation scheme, are: (A) locate a representation ...
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International Journal of Image and Data Fusion, 2015
The topic of BIG DATA is still causing much confusion and misunderstanding in the Geospatial/Geomatic community.
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The topic of BIG DATA is still causing much confusion and misunderstanding in the Geospatial/Geomatic community.
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