Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems

Distributed Target Tracking and Synchronization in Wireless Sensor Networks

Wireless sensor networks provide useful information for various applications but pose challenges in scalable information processing and network maintenance. This dissertation focuses on statistical methods for distributed information fusion and sensor synchronization for target tracking in wireless sensor networks. We perform target tracking using particle filtering. For scalability, we extend centralized particle filtering to distributed particle filtering via distributed fusion of local estimates provided by individual sensors. We derive a distributed fusion rule from Bayes\u27 theorem and implement it via average consensus. We approximate each local estimate as a Gaussian mixture and develop a sampling-based approach to the nonlinear fusion of Gaussian mixtures. By using the sampling-based approach in the fusion of Gaussian mixtures, we do not require each Gaussian mixture to have a uniform number of mixture components, and thus give each sensor the flexibility to adaptively learn a Gaussian mixture model with the optimal number of mixture components, based on its local information. Given such flexibility, we develop an adaptive method for Gaussian mixture fitting through a combination of hierarchical clustering and the expectation-maximization algorithm. Using numerical examples, we show that the proposed distributed particle filtering algorithm improves the accuracy and communication efficiency of distributed target tracking, and that the proposed adaptive Gaussian mixture learning method improves the accuracy and computational efficiency of distributed target tracking. We also consider the synchronization problem of a wireless sensor network. When sensors in a network are not synchronized, we model their relative clock offsets as unknown parameters in a state-space model that connects sensor observations to target state transition. We formulate the synchronization problem as a joint state and parameter estimation problem and solve it via the expectation-maximization algorithm to find the maximum likelihood solution for the unknown parameters, without knowledge of the target states. We also study the performance of the expectation-maximization algorithm under the Monte Carlo approximations used by particle filtering in target tracking. Numerical examples show that the proposed synchronization method converges to the ground truth, and that sensor synchronization significantly improves the accuracy of target tracking

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches

Probabilistic Methodology and Techniques for Artefact Conception and Development

The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

A framework for non-Gaussian signal modeling and estimation

"June 1999."Includes bibliographical references (p. [225]-240).Sponsored by the U.S. Air Force. F49620-96-1-0072 Supported by the U.S. Army Research Laboratory under Cooperative Agreement, DAAL01-96-2-001Shawn M. Verbout