Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 52 for “"particle filters"”.
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Parameter learning with particle filters
… An overview is provided of the recently proposed particle filter with accelerated adaptation which has demonstrated rapid time detection for changes in parameter values and has been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and …
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On particle filters in radar target tracking
… the research, implementation, and evaluation of particle filters for radar target track filtering of a maneuvering target, through quantitative simulations and analysis thereof. Target track filtering, also called target track smoothing, aims to minimize the error between a radar target's …
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Tracking sperm whales using passive acoustics and particle filters
… This is developed into a practical method using particle filters to track sperm whales. Sperm whales are the ideal subject species for this kind of development because the high sound pressure levels of their impulsive vocalisations (up to 236 dB re 1 ?Pa) makes them relatively simple to detect. …
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State space modelling of extreme values with particle filters
… time. Sequential Monte Carlo methods known as particle filters provide an approach to inference for such models whereby observations are added to the fit sequentially. Though originally developed for on-line inference, particle filters, along with related particle smoothers, often provide the …
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Novel Hybrid Resampling Algorithms for Parallel/Distributed Particle Filters
<p>Particle filters, also known as sequential Monte Carlo (SMC) methods, use the Bayesian inference and the stochastic sampling technique to estimate the states of dynamic systems from given observations. Parallel/Distributed particle filters were introduced to improve the performance of sequential …
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State estimation of probabilistic hybrid systems with particle filters
… state estimation that combines Rao-Blackwellised particle filtering with a Gaussian representation. Conceptually, our algorithm samples trajectories traced by the discrete variables over time and, for each trajectory, estimates the continuous state with a Kalman Filter. A key insight to handling …
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Towards smooth particle filters for likelihood estimation with multivariate latent variables
… parameters, the estimates produced by practical particle filters are not, even when common random numbers are used for each filter. This is because the same resampling step which drastically reduces the variance of the estimates also introduces discontinuities in the particles that are selected …
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Segmentation of nerve bundles and ganglia in spine MRI using particle filters
… for segmentation of nerve bundles based on particle filters. We develop a novel approach to flexible particle representation of tubular structures based on Bezier splines. We construct an appropriate dynamics to reflect the continuity and smoothness properties of real nerve bundles. …
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Full Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filters
… inspired nonlinear model. By using a particle filter to optimize the model parameters, the computation time is kept below a minute per voxel without requiring a linearization of the noise in the state variables. The activation results show regions similar to those found in Statistical …
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Blood-Oxygen-Level-Dependent Parameter Identification using Multimodal Neuroimaging and Particle Filters
… the fusion of this information is achieved in a Particle Filter (PF) framework. The trace plots and the correlation coefficients of the parameter estimates from the PF reflect ill-posedness of the BOLD model. The means of the parameter estimates are much closer to the ground truth compared to the …
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Recovering sample diversity in Rao-Blackwellized particle filters for simultaneous localization and mapping
… well-known failure mode of the Rao-Blackwellized particle filter (RBPF) in simultaneous localization and mapping (SLAMI) situations that arises when precise feature measurements yield a limited perceptual distribution relative to a motion-based proposal distribution. One set of solutions …
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A Control Architecture for Dynamic Execution of Robot Tasks Trained in Real-Time Using Particle Filters
… behaviors is implemented in real-time using a particle filter which allows for continuation of training if testing does not yield favorable results. Also a control architecture is designed and implemented to allow for execution of task sequences. This allows for larger, complicated tasks to be …
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The investigation of potentially toxic elements (PTE) and particulates absorbed on particle filters exposed to vehicle emissions at road level
… toxic elements (PTE) collected from cabin particle filters in and around Cork city. Cabin particle filters are used in motor vehicles to extract toxins from air coming through the ventilation system. A systematic study of pollutants captured over fixed mileages on cabin filters has not …
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Dynamic Bayesian networks for the classification of spinning discs
… thesis considers issues for the application of particle filters to a class of nonlinear filtering and classification problems. Specifically, we study a prototype system of spinning discs. The system combines linear dynamics describing rotation with a nonlinear observation model determined by the …
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Ensemble filtering for state space models
… designing efficient proposal distributions for particle filters. I propose a new approach named the augmented particle filter (APF), which combines two sets of particles from the observation and state equations. The APF can be applied to general state space models, and it does not require …
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Particle Filtering for Continuous Time Problems
… sequential Monte Carlo (SMC) methods, or particle filters, are used to estimate the posterior distribution in real time. A major challenge in particle filtering is estimating the hidden states of a stochastic system from noisy and incomplete observations, where both the state and …
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Direct Simulation Methods for Multiple Changepoint Problems.
… inference from such models by using the idea of particle filters. Compared to the existed methodology such as RJMCMC of Green (1995), the attraction of our particle filter is its simplicity and efficiency. We propose an on-line algorithm for exact filtering for a class of multiple changepoint …
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Robust Online Trajectory Prediction for Non-cooperative Small Unmanned Aerial Vehicles
… of predicted trajectories. This work adopts particle filters together with Löwner-John ellipsoid to approximate the highest posterior density region for trajectory prediction and uncertainty quantification. The particle filter is tuned and tested on real-world and simulated data sets and …
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Industrial Scalable Rolling Element Bearing Diagnostic and Prognostic Modelling
… this diagnostic model is then combined with particle filters and mathematical representations of the bearing degradation curve to estimate the remaining useful life of the bearing, with consideration for both the bearing load and speed.
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iNav : a hybrid approach to WiFi localization and tracking of mobile devices
… location estimation algorithm based on particle filters to integrate streams of WiFi access point observations and 3-axis accelerometer data. The system is tailored towards localization of vehicles and relies on a road network map to increase localization accuracy. iNav is designed with …
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