Robotic Sailing 2016 : Proceedings of the 9th International Robotic Sailing Conference

Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph. D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance. This book contains selected papers that address a variety of topics related to the design, development and operation of unmanned and fully autonomous sailing boats. These papers were presented in the 9th International Robotic Sailing Conference, in association with the 9th World Robotic Sailing Championship that took place in Viana do Castelo, Portugal from the 5th to 10th of September 2016. The book is divided in three parts, each focusing on key aspects of robotic sailing. The first part addresses the design, construction and validation of autonomous sailboat platforms, including their rigs, appendages and control mechanisms. The second part is devoted to the development of sensors and algorithms to enhance the performance of robotic sailing boats, in terms of their speed, course control and manoeuvring ability. Finally, the papers in the last part are dedicated to the improvement of behaviours required for the accomplishment of complex autonomous missions. Robotic sailing is a relatively new multidisciplinary area of research, with a recognized great potential for persistent ocean observation. Using the wind for boat propulsion is something mankind has been doing for centuries. Automating and optimizing the sailing process in the harsh marine environment is an ever present challenge which is now promising to bear fruit. "Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modelling, target tracking, and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters, and survival of the fittest, have made it possible to solve numerically many complex, nonstandard problems that were previously intractable. This book presents the first comprehensive and coherent treatment of these techniques, including convergence results and applictions to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, economometrics, financial modelling, neural networks, optimal control, optimal filtering, communicaitons, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis ... ." (source : 4ème de couverture)