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Relevant bibliographies by topics / Bayesian filter ; particle filtering ; tracking / Dissertations / Theses

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Dissertations / Theses on the topic 'Bayesian filter ; particle filtering ; tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Zhong, Xionghu. "Bayesian framework for multiple acoustic source tracking." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4752.

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Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is acti

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2

Torstensson, Johan, and Mikael Trieb. "Particle Filtering for Track Before Detect Applications." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4046.

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<p>Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. </p><p>This Master's thesis h

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3

Velmurugan, Rajbabu. "Implementation Strategies for Particle Filter based Target Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14611.

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This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter

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4

Bradley, Justin Mathew. "Particle Filter Based Mosaicking for Forest Fire Tracking." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2001.pdf.

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5

Koroglu, Muhammed Taha. "Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.

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6

Karlsson, Rickard. "Particle filtering for positioning and tracking applications /." Linköping : Dept. of Electrical Engineering, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek924s.pdf.

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7

Wu, Jiande. "Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1953.

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Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task. Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and ev

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8

Ozkan, Emre. "Particle Methods For Bayesian Multi-object Tracking And Parameter Estimation." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf.

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In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method<br>the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based par

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9

Ackerman, Samuel. "A Probabilistic Characterization of Shark Movement Using Location Tracking Data." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/499173.

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Statistics<br>Ph.D.<br>Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the

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10

Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.

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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as ran

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11

Lin, Chung-Ching. "Detecting and tracking moving objects from a moving platform." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/49014.

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Detecting and tracking moving objects are important topics in computer vision research. Classical methods perform well in applications of steady cameras. However, these techniques are not suitable for the applications of moving cameras because the unconstrained nature of realistic environments and sudden camera movement makes cues to object positions rather fickle. A major difficulty is that every pixel moves and new background keeps showing up when a handheld or car-mounted camera moves. In this dissertation, a novel estimation method of camera motion parameters will be discussed first. Based

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12

Ozkaya, Bora. "Application, Comparison, And Improvement Of Known Received Signal Strength Indication (rssi) Based Indoor Localization And Tracking Methods Using Active Rfid Devices." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613014/index.pdf.

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Localization and tracking objects or people in real time in indoor environments have gained great importance. In the literature and market, many different location estimation and tracking solutions using received signal strength indication (RSSI) are proposed. But there is a lack of information on the comparison of these techniques revealing their weak and strong behaviors over each other. There is a need for the answer to the question<br>&ldquo<br>which localization/tracking method is more suitable to my system needs?&rdquo<br>. So, one purpose of this thesis is to seek the answer to this que

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13

Lee, Yeongseon. "Bayesian 3D multiple people tracking using multiple indoor cameras and microphones." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29668.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Rusell M. Mersereau; Committee Member: Biing Hwang (Fred) Juang; Committee Member: Christopher E. Heil; Committee Member: Georgia Vachtsevanos; Committee Member: James H. McClellan. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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14

Oh, Seung-Min. "Nonlinear Estimation for Vision-Based Air-to-Air Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19882.

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Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownshi

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15

Pace, Michele. "Stochastic models and methods for multi-object tracking." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00651396.

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La poursuite multi-cibles a pour objet le suivi d'un ensemble de cibles mobiles à partir de données obtenues séquentiellement. Ce problème est particulièrement complexe du fait du nombre inconnu et variable de cibles, de la présence de bruit de mesure, de fausses alarmes, d'incertitude de détection et d'incertitude dans l'association de données. Les filtres PHD (Probability Hypothesis Density) constituent une nouvelle gamme de filtres adaptés à cette problématique. Ces techniques se distinguent des méthodes classiques (MHT, JPDAF, particulaire) par la modélisation de l'ensemble des cibles comm

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16

Legrand, Leo. "Contributions aux pistages mono et multi-cibles fondés sur les ensembles finis aléatoires." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0107/document.

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La détection et le pistage de cibles de surface, maritimes ou terrestres, constituent l’un des champs d’application de la surveillance par radar aéroporté. Dans ce contexte spécifique, il s’agit d’estimer les trajectoires d’un ou de plusieurs objets mobiles au cours du temps à partir de mesures radar bruitées. Cependant, plusieurs contraintes s’additionnent au problème d’estimation des trajectoires :1. le nombre d’objets présents dans la région d’intérêt est inconnu et peut évoluer au cours du temps,2. les mesures fournies par le radar ne correspondent pas toutes à des objets mobiles car certa

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17

Lamberti, Roland. "Contributions aux méthodes de Monte Carlo et leur application au filtrage statistique." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLL007/document.

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Cette thèse s’intéresse au problème de l’inférence bayésienne dans les modèles probabilistes dynamiques. Plus précisément nous nous focalisons sur les méthodes de Monte Carlo pour l’intégration. Nous revisitons tout d’abord le mécanisme d’échantillonnage d’importance avec rééchantillonnage, puis son extension au cadre dynamique connue sous le nom de filtrage particulaire, pour enfin conclure nos travaux par une application à la poursuite multi-cibles.En premier lieu nous partons du problème de l’estimation d’un moment suivant une loi de probabilité, connue à une constante près, par une méthode

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18

Benko, Matej. "Hledaní modelů pohybu a jejich parametrů pro identifikaci trajektorie cílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445467.

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Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský

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19

Srinivasamurthy, Ajay. "A Data-driven bayesian approach to automatic rhythm analysis of indian art music." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/398986.

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Las colecciones de música son cada vez mayores y más variadas, haciendo necesarias nuevas fórmulas para su organización automática. El análisis automático del ritmo tiene como fin la extracción de información rítmica de grabaciones musicales y es una de las principales áreas de investigación en la disciplina de recuperación de la información musical (MIR por sus siglas en inglés). La dimensión rítmica de la música es específica a una cultura y por tanto su análisis requiere métodos que incluyan el contexto cultural. Las complejidades rítmicas de la música clásica de la India, una de las may

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20

Klimeš, Ondřej. "Komprimované vzorkování pro efektivní sledování objektu senzorovou sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400432.

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The master's thesis deals with target tracking. For this a decentralized sensor network using distributed particle filter with likelihood consensus is used. This consensus is based on a sparse representation of local likelihood function in a suitable chosen dictionary. In this thesis two dictionaries are compared: the widely used Fourier dictionary and our proposed B-splines. At the same time, thanks to the sparsity of distributed data, it is possible to implement compressed sensing method. The results are compared in terms of tracking error and communication costs. The thesis also contains sc

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21

Oulad, Ameziane Mehdi. "Amélioration de l'exploration de l'espace d'état dans les méthodes de Monte Carlo séquentielles pour le suivi visuel." Thesis, Ecole centrale de Lille, 2017. http://www.theses.fr/2017ECLI0007.

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Le suivi visuel constitue une tâche essentielle en vision par ordinateur. Les approches Bayésiennes sont largement utilisées aujourd’hui pour résoudre la problématique du suivi visuel. Notamment grâce aux possibilités offertes par les méthodes de Monte Carlo séquentielles (SMC) qui prennent en comptes les incertitudes du model et s’adaptent à des scenarios variés. L’efficacité des méthodes SMC dépend fortement du choix de la loi de proposition qui permet d’explorer l’espace d’état.Dans cette thèse, nous cherchons à améliorer l’exploration de l’espace d’état en approchant la loi de proposition

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22

Datta, Gupta Syamantak. "A Comparative Study of the Particle Filter and the Ensemble Kalman Filter." Thesis, 2009. http://hdl.handle.net/10012/4503.

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Non-linear Bayesian estimation, or estimation of the state of a non-linear stochastic system from a set of indirect noisy measurements is a problem encountered in several fields of science. The particle filter and the ensemble Kalman filter are both used to get sub-optimal solutions of Bayesian inference problems, particularly for high-dimensional non-Gaussian and non-linear models. Both are essentially Monte Carlo techniques that compute their results using a set of estimated trajectories of the variable to be monitored. It has been shown that in a linear and Gaussian environment, solutions o

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23

Ma, Hui. "Collaborative information processing techniques for target tracking in wireless sensor networks." 2008. http://hdl.handle.net/2440/49462.

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Target tracking is one of the typical applications of wireless sensor networks: a large number of spatially deployed sensor nodes collaboratively sense, process and estimate the target state (e.g., position, velocity and heading). This thesis aimed to develop the collaborative information processing techniques that jointly address information processing and networking for the distributive estimation of target state in the highly dynamic and resources constrained wireless sensor networks. Taking into account the interplay between information processing and networking, this thesis proposed a col

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24

Boddikurapati, Sirish. "Sequential Monte Carlo Methods With Applications To Communication Channels." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

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Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal processing, communications, statistics and econometrics. Recursive filtering is one way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Bayesian methods provide a general framework for dynamic state estimation problems. The central idea behind this recursive Bayesian estimation is computing the probability density function of the state vector of the system conditioned on the

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