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Showing 1 to 20 of 120 for “"Particle Filter"”.
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Feedback particle filter and its applications
The purpose of nonlinear filtering is to extract useful information from noisy sensor data. It finds applications in all disciplines of science and engineering, including tracking and navigation, traffic surveillance, financial engineering, neuroscience, biology, robotics, computer vision, weather …
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Particle Filter Based Mosaicking for Forest Fire Tracking
… attempt to find single geolocation estimates and filter that estimate with subsequent observations. While this is an effective method of resolving the noise to achieve a better geolocation estimate, it reduces a fire to a single point or small set of points. A georeferenced mosaic is a more …
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Localizing external contact using proprioceptive sensors : the contact particle filter
… This paper introduces the CPF, the Contact Particle Filter, which is a general algorithm for detecting and localizing external contacts on rigid body robots without the need for external sensing. The CPF finds external contact points that best explain the observed external joint torque, and …
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State estimation for a holonomic omniwheel robot using a particle filter
… A custom robot platform was designed to use a Particle Filter to estimate state. The motor controller was developed to control robot vectoring and report odometry, and noise analysis on an absolute positioning system, Ubisense, was performed to characterize the system. High frequency noise …
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Particle filter based tracking in a detection sparse discrete event simulation environment
… gaming artificial intelligence suggest that particle-based tracking techniques can provide accurate and computationally efficient state estimation of opposing agents within virtual environments. In this work several particle-based methods for obtaining and tracking contacts are explored to …
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Indoor Location Tracking and Orientation Estimation Using a Particle Filter, INS, and RSSI
… location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results.</p>
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Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter
… shown that importance sampling can be avoided in particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control and mean-field game formalisms. The resulting algorithm is referred to as feedback particle filter (FPF). The purpose of this …
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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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Ensemble filtering for state space models
… oceanography, and tomography. The goal of the filtering problem is to find the posterior distribution of the hidden state given the current and past observations. The first part of my thesis focuses on designing efficient proposal distributions for particle filters. I propose a new approach …
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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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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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Essays on Measuring Monetary Policy Uncertainty and Forecasting Business Cycle
… field of a Monte Carlo simulation based method: particle filter. Particle filter can be applied to many flexible state space models such as non-linear, non-Gaussian, stochastic volatility models or stochastic volatility models with zero lower bound. These models have become increasingly popular …
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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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Dynamic action spaces for autonomous search operations
… a contact position distribution from a generic particle filter to estimate the state of a single moving contact and to plan the path that minimizes the uncertainty in the location of the contact. The search algorithms introduced in this thesis will implement a motion planner that searches for a …
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Tracking moving objects in surveillance video
… using recursive Bayesian estimation. The particle filter is a technique for implementing such a recursion and so it is examined in the context of both single target and combined multi-target tracking. A detailed examination of the operation of the single target tracking particle filter …
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Target Tracking Via Marine Radar
… Most widely used tracking techniques are Kalman filter and particle filter. These filters use data with random errors and estimate accurate values for the current state of the system. Kalman filter is a linear estimator which does not depend on a set of past observations and hence efficient in …
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Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte …
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Physics-Informed Diagnostic and Prognostic Models for Rolling Element Bearings Using Oil Debris Data
… thoroughly evaluates the effectiveness of the particle filter and its variants. Subsequently, the enhanced version of the Auxiliary Particle Filter with Resample Move is deployed to estimate the remaining useful life. The paramount significance of this developed model lies in its adaptability …
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City-scale Cross-view Geolocalization with Generalization to Unseen Environments
… (ReWAG) that combine a neural network with a particle filter to achieve global position estimates for a moving agent in a GPS-denied environment while scaling efficiently to city-sized regions in unseen environments and working with either panoramic or non-panoramic cameras. One contribution …
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