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 59 for “"Particle filtering"”.
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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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Particle Filtering for Stochastic Control and Global Optimization
… and global optimization through the use of particle filtering. Stochastic control and global optimization are two areas that have many applications but are often difficult to solve. In stochastic control, an important class of problems, namely, partially observable Markov decision processes …
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Particle Filtering and Optimal Control for Vehicles and Robots
… This formulation is utilized when developing two particle-filter algorithms for the OOSM problem. The algorithms improve estimation accuracy and tracking robustness, compared with methods that do not utilize the linear substructure. A second topic is sensor fusion for improved autonomy in …
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Partitioned particle filtering for target tracking in video sequences
[page 9-12,17,18 are missing] A partitioned particle filtering algorithm is developed to track moving targets exhibiting complex interaction in a static environment, in a video sequence. The filter is augmented with an additional scan phase, which is a deterministic sequence which has been …
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Particle filtering for EEG source localization and constrained state spaces
Particle Filters (PFs) have a unique ability to perform asymptotically optimal estimation for non-linear and non-Gaussian state-space models. However, the numerical nature of PFs cause them to have major weakness in two important areas: (1) handling constraints on the state, and (2) dealing with …
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Particle filtering with Lagrangian data in a point vortex model
Particle filtering is a technique used for state estimation from noisy measurements. In fluid dynamics, a popular problem called Lagrangian data assimilation (LaDA) uses Lagrangian measurements in the form of tracer positions to learn about the changing flow field. Particle filtering can be applied …
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Particle filtering for frequency estimation from acoustic time-series in dispersive media
… on the development of sequential Bayesian filtering methods to obtain accurate estimates of instantaneous frequencies using Short Term Fourier Transforms within the acoustic field measured at an array of hydrophones, which can be used in a subsequent step for the estimation of propagation …
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A Particle Filtering Approach to Joint Passive Radar Tracking and Target Classification
… class identity and target orientation, and (3) a particle filter-based implementation that takes into account realistic difficulties caused by multiple targets, false alarms, and missed detections.
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Latent State and Parameter Estimation of Stochastic Volatility/Jump Models via Particle Filtering
Particle filtering in stochastic volatility/jump models has gained significant attention in the last decade, with many distinguished researchers adding their contributions to this new field. Golightly (2009), Carvalho et al. (2010), Johannes et al. (2009) and Aihara et al. (2008) all attempt to …
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Resource-Aware Distributed Particle Filtering for Cluster-Based Object Tracking in Wireless Camera Networks
… framework for the implementation of distributed particle filters in resource-constrained WCNs. Our method focuses on the effects of communication failures on object tracking performance by adjusting the amount of data packets generated and transmitted by the cameras according to the network …
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Energy Efficient Target Tracking in Wireless Sensor Networks: Sleep Scheduling, Particle Filtering, and Constrained Flooding
… target tracking in WSNs: sleep scheduling, particle filtering, and constrained flooding. We develop a Target Prediction and Sleep Scheduling protocol (TPSS) to improve energy efficiency for idle listening. We start with designing a target prediction method based on both kinematics and …
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Distributed Target Tracking and Synchronization in Wireless Sensor Networks
… <p>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' theorem and …
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Nonlinear filtering of high dimensional, chaotic, multiple timescale correlated systems
… theoretical and numerical questions in nonlinear filtering theory for high dimensional, chaotic, multiple timescale correlated systems. The research is motivated by problems in the geosciences, in particular oceanic or atmospheric estimation and climate prediction. As the capability and need to …
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Multirobot Tethering for Localization and Control
Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low …
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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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Inverse methods for sound speed estimation in the ocean
… of noise in the arrival times. Subsequently, particle filtering is employed for the estimation of arrival times from signals recorded during the Shallow Water 06 experiment. It has been shown in the past that particle filtering can be employed for the successful estimation of multipath arrival …
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Real-time Traffic State Prediction: Modeling and Applications
… the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of …
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Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks
This thesis is based on 5 papers exploring the filtering problem in non-linear non-Gaussian state-space models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks. The aim of the first paper is to study the …
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Comparison of nonlinear filtering techniques
… that importance sampling can be avoided in the particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control formalisms. The resulting algorithm is referred to as feedback particle filter. The purpose of this thesis is to provide a …
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