The minimal representation approach to multisensor fusion is based on the use of an information measure as a universal yardstick for fusion. Using models of sensor uncertainty, the representation size guides the integration of widely varying types of data and maximizes the information contributed to a consistent interpretation.
In this book, the general theory of minimal representation multisensor fusion is developed and applied in a series of experimental studies of sensor-based robot manipulation. A novel application of differential evolutionary computation is introduced to achieve practical and effective solutions to this difficult computational problem.
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The minimal representation approach to multisensor fusion is based on the use of an information measure as a universal yardstick for fusion. Using models of sensor uncertainty, the representation size guides the integration of widely varying types of data and maximizes the information contributed to a consistent interpretation.
In this book, the general theory of minimal representation multisensor fusion is developed and applied in a series of experimental studies of sensor-based robot manipulation. A novel application of differential evolutionary computation is introduced to achieve practical and effective solutions to this difficult computational problem.
Imprint | World Scientific Publishing Co Pte Ltd |
Country of origin | Singapore |
Series | Series In Intelligent Control And Intelligent Automation, 11 |
Release date | December 1999 |
Availability | Supplier out of stock. If you add this item to your wish list we will let you know when it becomes available. |
First published | December 1999 |
Authors | Rajive Joshi, Arthur C. Sanderson |
Dimensions | 230 x 162 x 23mm (L x W x T) |
Format | Hardcover |
Pages | 336 |
ISBN-13 | 978-981-02-3880-3 |
Barcode | 9789810238803 |
Categories | |
LSN | 981-02-3880-0 |