Essays about: "sequential importance resampling"
Found 4 essays containing the words sequential importance resampling.
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1. KL/TV Reshuffling : Statistical Distance Based Offspring Selection in SMC Methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Over the years sequential Monte Carlo (SMC), and, equivalently, particle filter (PF) theory has enjoyed much attention from researchers. However, the intensity of developing innovative resampling methods, also known as offspring selection methods, has long been declining, with most of the popular schemes aging back two decades. READ MORE
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2. Vehicle Collision Risk Prediction Using a Dynamic Bayesian Network
University essay from KTH/Matematisk statistikAbstract : This thesis tackles the problem of predicting the collision risk for vehicles driving in complex traffic scenes for a few seconds into the future. The method is based on previous research using dynamic Bayesian networks to represent the state of the system. READ MORE
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3. Parallel Hardware for Sampling Based Nonlinear Filters in FPGAs
University essay from Linköpings universitet/Elektroniksystem; Linköpings universitet/Tekniska högskolanAbstract : Particle filters are a class of sequential Monte-Carlo methods which are used commonly when estimating various unknowns of the time-varying signals presented in real time, especially when dealing with nonlinearity and non-Gaussianity in BOT applications. This thesis work is designed to perform one such estimate involving tracking a person using the road information available from an IR surveillance video. READ MORE
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4. Particle Methods for Indoor Tracking in WiFi Networks
University essay from Lunds universitet/Matematisk statistikAbstract : This thesis treats the problem of positioning in WiFi networks and proposes a solution using hidden Markov models and particle lters based on sequential importance sampling with resampling. Hidden Markov models prove to be a powerful framework for this type of problem exhibiting both an intuitive and adaptive model structure. READ MORE