A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements

Multipath propagation causes major impairments to Global Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

A Fixed-lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation

Interferences are among the most penalizing error sources in Global Positioning System (GPS) navigation. So far, many effort has been devoted to developing GPS receivers more robust to the radiofrequency environment. Contrary to previous approaches, this paper does not aim at improving the estimation of the GPS pseudoranges between the mobile and the GPS satellites in the presence of interferences. As an alternative, we propose to model interference effects as variance jumps affecting the GPS measurements which can be directly detected and compensated at the level of the navigation algorithm. Since the joint detection/estimation of the interference errors and motion parameters is a highly non linear problem, a particle filtering technique is used. An original particle filter is developed to improve the detection performance while ensuring a good accuracy of the positioning solution

Advances in point process filters and their application to sympathetic neural activity

This thesis is concerned with the development of techniques for analyzing the sequences of stereotypical electrical impulses within neurons known as spikes. Sequences of spikes, also called spike trains, transmit neural information; decoding them often provides details about the physiological processes generating the neural activity. Here, the statistical theory of event arrivals, called point processes, is applied to human muscle sympathetic spike trains, a peripheral nerve signal responsible for cardiovascular regulation. A novel technique that uses observed spike trains to dynamically derive information about the physiological processes generating them is also introduced. Despite the emerging usage of individual spikes in the analysis of human muscle sympathetic nerve activity, the majority of studies in this field remain focused on bursts of activity at or below cardiac rhythm frequencies. Point process theory applied to multi-neuron spike trains captured both fast and slow spiking rhythms. First, analysis of high-frequency spiking patterns within cardiac cycles was performed and, surprisingly, revealed fibers with no cardiac rhythmicity. Modeling spikes as a function of average firing rates showed that individual nerves contribute substantially to the differences in the sympathetic stressor response across experimental conditions. Subsequent investigation of low-frequency spiking identified two physiologically relevant frequency bands, and modeling spike trains as a function of hemodynamic variables uncovered complex associations between spiking activity and biophysical covariates at these two frequencies. For example, exercise-induced neural activation enhances the relationship of spikes to respiration but does not affect the extremely precise alignment of spikes to diastolic blood pressure. Additionally, a novel method of utilizing point process observations to estimate an internal state process with partially linear dynamics was introduced. Separation of the linear components of the process model and reduction of the sampled space dimensionality improved the computational efficiency of the estimator. The method was tested on an established biophysical model by concurrently computing the dynamic electrical currents of a simulated neuron and estimating its conductance properties. Computational load reduction, improved accuracy, and applicability outside neuroscience establish the new technique as a valuable tool for decoding large dynamical systems with linear substructure and point process observations

Extending expectation propagation for graphical models

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 101-106).Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to develop efficient Bayesian inference and learning algorithms for graphical models. The first two chapters of the thesis present inference algorithms for generative graphical models, and the next two propose learning algorithms for conditional graphical models. First, the thesis proposes a window-based EP smoothing algorithm for online estimation on hybrid dynamic Bayesian networks. For an application in wireless communications, window-based EP smoothing achieves estimation accuracy comparable to sequential Monte Carlo methods, but with less than one-tenth computational cost. Second, it develops a new method that combines tree-structured EP approximations with the junction tree for inference on loopy graphs. This new method saves computation and memory by propagating messages only locally to a subgraph when processing each edge in the entire graph. Using this local propagation scheme, this method is not only more accurate, but also faster than loopy belief propagation and structured variational methods. Third, it proposes predictive automatic relevance determination (ARD) to enhance classification accuracy in the presence of irrelevant features. ARD is a Bayesian technique for feature selection.(cont.) The thesis discusses the overfitting problem associated with ARD, and proposes a method that optimizes the estimated predictive performance, instead of maximizing the model evidence. For a gene expression classification problem, predictive ARD outperforms previous methods, including traditional ARD as well as support vector machines combined with feature selection techniques. Finally, it presents Bayesian conditional random fields (BCRFs) for classifying interdependent and structured data, such as sequences, images or webs. BCRFs estimate the posterior distribution of model parameters and average prediction over this posterior to avoid overfitting. For the problems of frequently-asked-question labeling and of ink recognition, BCRFs achieve superior prediction accuracy over conditional random fields trained with maximum likelihood and maximum a posteriori criteria.by Yuan Qi.Ph.D