Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

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.

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Sequential Monte Carlo methods in practice

Planning and navigation algorithms exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.

Probabilistic Robotics

A Bayesian technique is applied to the target acquisition problem at handover from the fire control to the missile seeker. This is a multiple hypothesis scheme which includes an explicit model of the possible misalignment or bias between the fire control and seeker coordinate frames. This method is compared with a linear least cost assignment technique which may be implemented via the Munkres fast search algorithm.

Target acquisition/selection: a comparison between a Bayesian technique and the Munkres algorithm

The Bayesian filter discussed in Chap. 3 relies on knowledge of three probability density functions: the state prior distribution, the state stochastic model, and the measurement conditional probability density.

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Measurement Model, Satellite Communications

Previous papers have described the use of a Long Period Grating (LPG) as a simple photonic level sensor. We show how a useful performance of 1 mm resolution over 1 m could be obtained in a system that requires only a low-cost interrogator rather than an Optical Spectrum Analyzer.

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Development of a photonic gauging sensor for hazardous liquids (Summary)

In this paper, technology developed at the University of Strathclyde which has resulted in the commercialisation of distributed point sensors for electrical and mechanical parameters will be described. While primarily targeted at terrestrial power system applications to date, this technology has significant potential for realising novel and effective monitoring and protection solutions for marine electrical systems. In the paper, the technology and its capabilities will be reviewed, and a number of potential applications will be described and discussed briefly. Many of these applications can assist in reducing the levels of risk associated with marine electrical systems, through delivering comprehensive monitoring and protection functions that benefit from the distributed nature of the measurement system. The measurement system will be outlined and it will be shown how it consists of a central interrogator that uses optical fibre to collect several measurements from a distributed, and, importantly, completely passive, array of sensors that are connected along the length of the fibre, which can be up to 100 km in length (clearly adequate for any marine applications). Finally, the paper will describe a number of distributed monitoring and protection applications in marine electrical systems that can be facilitated by the distributed measurement system.

Distributed sensing for marine electrical system monitoring and protection applications

Surface nanoscale axial photonics (SNAP) structures are created with a femtosecond laser inscription for the first time. A micron-thin inscription, introduced along the fiber axis, is characterized by the measured subnanometer-small effective fiber radius variation.

Neil Gordon

Papers

Target acquisition/selection: a comparison between a Bayesian technique and the Munkres algorithm

Measurement Model, Satellite Communications

Development of a photonic gauging sensor for hazardous liquids (Summary)

Distributed sensing for marine electrical system monitoring and protection applications

Surface nanoscale axial photonics (SNAP) structures introduced with a femtosecond laser