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Multi-Sensor Data Fusion with MATLAB by Jitendra R. Raol

Multi-Sensor Data Fusion with MATLAB Jitendra R. Raol ebook
ISBN: 1439800030, 9781439800034
Publisher:
Format: pdf
Page: 568

Dec 25, 2013 - It is an extensively revised second edition of the author's successful book: “Multi-Sensor Data Fusion: An Introduction” which was originally published by Springer-Verlag in 2007. Feb 1, 2002 - Additionally, dedicated MATLAB functions/programs have been developed for each chapter to further enhance the understanding of the theory, and provide a source for establishing radar system design requirements. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. The layout and typography has been revised. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. This book includes over 1190 equations and over 230 illustrations and plots. The main changes in the new book are: New Material: Apart from one new Layout. Feb 25, 2009 - For parameter adjustment, the sensor data acquisition and fusion algorithms were carried out off-line in MATLAB-SIMULINK® (The Mathworks, Natick, MA), while the real time control algorithms were finally implemented in a Freescale® .. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk. There are It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by Springer-Verlag in 2007. Feb 20, 2010 - An Autobiography" by Donald K Slayton or other books in the Engineering > Aeronautical Engineering category, you might like to know that "Multi-Sensor Data Fusion with MATLAB: Theory and Practice" is now available. Oct 26, 2010 - Per Slycke, CTO of Xsens explains; “Measuring 3D motion accurately in biomechanics research, sports and ergonomics is already challenging – you do not want time synchronization between multiple sensors to be a potential cause Xsens' research department has created unique intellectual property in the field of multi-sensor data fusion algorithms, combining inertial sensors with aiding technologies such as GPS and RF positioning and biomechanical modeling. Using MATLAB, computational loads of these methods are compared while number of sensors increases.