Neil Gordon

Bio: Neil Gordon is an academic researcher from Aston University. The author has contributed to research in topics: Particle filter & Negative luminescence. The author has an hindex of 37, co-authored 181 publications receiving 37011 citations. Previous affiliations of Neil Gordon include Qinetiq & University of Cambridge.

A hybrid bootstrap filter for target tracking in clutter

Abstract: The problem of tracking multiple targets with multiple sensors in the presence of interfering measurements is considered A new hybrid bootstrap filter is proposed The bootstrap filter is an approach where random samples are used to represent the target posterior distributions By using this approach, the author circumvents the usual problem of an exponentially increasing number of association hypotheses as well as allowing the use of any nonlinear/non-Gaussian system and/or measurement models

Single-Ended Differential Protection in MTDC Networks Using Optical Sensors

Abstract: This paper presents a method for rapid detection of faults on voltage source converter multiterminal HVdc transmission networks using multipoint optical current sensing. The proposed method uses differential protection as a guiding principle, and is implemented using current measurements obtained from the optical current sensors distributed along the transmission line. Performance is assessed through detailed transient simulation using MATLAB/Simulink models, integrating inductive dc-line terminations, detailed dc circuit-breaker models, and a network of fiber-optic current sensors. Moreover, the feasibility and required performance of optical-based measurements is validated through laboratory testing. Simulation results demonstrate that the proposed protection algorithm can effectively, and within very short period of time, discriminate between faults on the protected line (internal faults), and those occurring on adjacent lines or busbars (external faults). Hardware tests prove that the scheme can be achieved with the existing, available sensing technology.

A variable structure multiple model particle filter for GMTI tracking

Abstract: The problem of tracking ground targets with GMTI sensors has received some attention in the recent past. In addition to standard GMTI sensor measurements, one is interested in using non-standard information such as road maps, and terrain-related visibility conditions to enhance tracker performance. The conventional approach to this problem has been to use the variable structure IMM (VS-IMM), which uses the concept of directional process noise to model motion along particular roads. In this paper, we present a particle filter based approach to this problem which we call variable structure multiple model particle filter (VS-MMPF). Simulation results show that the performance of the VS-MMPF is much superior to that of VS-IMM.

Efficient particle filters for joint tracking and classification

Abstract: Target tracking is usually performed using data from sensors such as radar, whilst the target identification task usually relies on information from sensors such as IFF, ESM or imagery. The differing nature of the data from these sensors has generally led to these two vital tasks being performed separately. However, it is clear that an experienced operator can observe behavior characteristics of targets and, in combination with knowledge and expectations of target type and likely activity, can more knowledgeably identify the target and robustly predict its track than any automatic process yet defined. Most trackers are designed to follow targets within a wide envelope of trajectories and are not designed to derive behavior characteristics or include them as part of their output. Thus, there is potential scope for both applying target type knowledge to improve the reliability of the tracking process, and to derive behavioral characteristics which may enhance knowledge about target identity and/or activity. In this paper we introduce a Bayesian framework for joint tracking and identification and give a robust and computationally efficient particle filter based algorithm for numerical implementation of the resulting recursions. Simulation results illustrating algorithm performance are presented.

A comparison of particle filters for recursive track-before-detect

Abstract: Track-before-detect is a powerful technique for detection and tracking of targets with low signal-to-noise ratio. This paper presents a performance comparison of two particle filters recently proposed for this application using several different particle proposal densities designed for track initiation. The first particle filter is a standard SIR particle filter, which treats the track-before-detect problem as a hybrid estimation problem by incorporating a discrete random variable, "target existence," into the state vector. The second particle filter formulates the probability of existence calculation in a different way, avoiding the need for hybrid estimation. Three different particle proposal densities are considered, which are designed to compare performance when the data is used to aid in particle proposal.

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

Abstract: 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.

Pattern Recognition and Machine Learning

Abstract: 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.

Machine Learning : A Probabilistic Perspective

Abstract: 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.

Sequential Monte Carlo methods in practice

Abstract: 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.

Probabilistic Robotics

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