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Dissertations / Theses on the topic 'Particle filter'

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Published: 4 June 2021
Last updated: 5 October 2024

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1

Cao, Hui. "Smoothed Particle Filter." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10425.

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2

Morzfeld, Matthias, Daniel Hodyss, and Chris Snyder. "What the collapse of the ensemble Kalman filter tells us about particle filters." TAYLOR & FRANCIS LTD, 2017. http://hdl.handle.net/10150/623125.

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The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.
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3

Rane, Nikhil. "Isomap tracking with particle filter." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1181252052/.

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4

Xia, Gongyi. "Particle Swarm Optimization and Particle Filter Applied to Object Tracking." Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27610.

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The particle filter is usually used as a tracking algorithm in non-linear under the Bayesian tracking framework. However, the problems of degeneracy and impoverishment degrade its performance. The particle filter is thereafter enhanced by evolutionary optimization, in particular, Particle Swarm Optimization (PSO) is used in this thesis due to its capability of optimizing non-linear problems. In this thesis, the PSO enhanced particle filter is reviewed followed by an analysis of its drawbacks. Then, a novel sampling mechanism for the particle filter is proposed. This method generates particles via the PSO process and estimates the importance distribution from all the particles generated. This ensures that particles are located in high likelihood regions while still maintaining a certain level of diversity. This sampling mechanism is then used together with the marginal particle filter. The proposed method?s superiority in performance over the conventional particle filter is then demonstrated by simulations.
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Gebart, Joakim. "GPU Implementation of the Particle Filter." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94190.

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This thesis work analyses the obstacles faced when adapting the particle filtering algorithm to run on massively parallel compute architectures. Graphics processing units are one example of massively parallel compute architectures which allow for the developer to distribute computational load over hundreds or thousands of processor cores. This thesis studies an implementation written for NVIDIA GeForce GPUs, yielding varying speed ups, up to 3000% in some cases, when compared to the equivalent algorithm performed on CPU. The particle filter, also known in the literature as sequential Monte-Carlo methods, is an algorithm used for signal processing when the system generating the signals has a highly nonlinear behaviour or non-Gaussian noise distributions where a Kalman filter and its extended variants are not effective. The particle filter was chosen as a good candidate for parallelisation because of its inherently parallel nature. There are, however, several steps of the classic formulation where computations are dependent on other computations in the same step which requires them to be run in sequence instead of in parallel. To avoid these difficulties alternative ways of computing the results must be used, such as parallel scan operations and scatter/gather methods. Another area where parallel programming still is not widespread is the area of pseudo-random number generation. Pseudo-random numbers are required by the algorithm to simulate the process noise as well as for avoiding the particle depletion problem using a resampling step. In this thesis a recently published counter-based pseudo-random number generator is used.
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Käll, Viktor, and Erik Piscator. "Particle Filter Bridge Interpolation in GANs." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301733.

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Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. This similarity measure gives rise to the possibility of interpolating in the data which has been done successfully in the past. Herein we propose a new stochastic interpolation method for GANs where the interpolation is forced to adhere to the data distribution by implementing a sequential Monte Carlo algorithm for data sampling. The results show that the new method outperforms previously known interpolation methods for the data set LINES; compared to the results of other interpolation methods there was a significant improvement measured through quantitative and qualitative evaluations. The developed interpolation method has met its expectations and shown promise, however it needs to be tested on a more complex data set in order to verify that it also scales well.
Generative adversarial networks (GANs) är ett slags generativ modell som har fått mycket uppmärksamhet de senaste åren sedan de upptäcktes för sin potential att återskapa komplexa högdimensionella datafördelningar. Dessa förser en komprimerad representation av datan där enbart de karaktäriserande egenskaperna är bevarade, vilket följdaktligen inducerar ett avståndsmått på datarummet. Detta avståndsmått möjliggör interpolering inom datan vilket har åstadkommits med framgång tidigare. Häri föreslår vi en ny stokastisk interpoleringsmetod för GANs där interpolationen tvingas följa datafördelningen genom att implementera en sekventiell Monte Carlo algoritm för dragning av datapunkter. Resultaten för studien visar att metoden ger bättre interpolationer för datamängden LINES som användes; jämfört med resultaten av tidigare kända interpolationsmetoder syntes en märkbar förbättring genom kvalitativa och kvantitativa utvärderingar. Den framtagna interpolationsmetoden har alltså mött förväntningarna och är lovande, emellertid fordras att den testas på en mer komplex datamängd för att bekräfta att den fungerar väl även under mer generella förhållanden.
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Johansson, Henrik. "Road-constrained target tracking using particle filter." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11562.

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In this work a particle filter (PF) that uses a one-dimensional dynamic model to estimate the position of vehicles traveling on a road is derived. The dynamic model used in the PF is a second order linear-Gaussian model. To be able to track targets traveling both on and off road two different multiple model filters are proposed. One of the filters is a modified version of the Efficient Interacting Multiple Model (E-IMM) and the other is a version of the Multiple Likelihood Models (MLM). Both of the filters uses two modes, one for the on road motion and one for the off road motion. The E-IMM filter and the MLM filter are compared to the standard PF to be able to see the performance gain in using multiple models. This result indicates that the multiple model filters have better performance, at least when the true mode switching probabilities are used.


Den här arbetet presenterar ett partikelfilter som använder sig av en endimensionell dynamisk modell för att skatta positionen på fordon som befinner sig på någon väg. Den dynamiska modellen som används i partikelfiltret är en andra ordningens linjär-gaussisk modell. För att kunna spåra fordon som befinner sig både på och utanför vägen så föreslås två olika multipla filter. Ena filtret är en modifierad

variant av Efficient Interacting Multiple Model (E-IMM) och den andra är en version a Multiple Likelihood Models (MLM). Båda filtren använder sig av två moder, en för rörelse på vägen och en för rörelse utanför vägen. E-IMM filtret och MLM filtret jämförs med ett standard partikelfilter för att kunna se förbättringen vid använding av multipla modeller. Resultatet visar att båda multipla modell filtren ger bättre resultat, i varje fall då rätt sannolikheter för modbyte används.

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8

Tonetto, Leonardo. "A Particle Filter approach to GPS signals." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177320.

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Localization systems nowadays are extensively used by a growing number of mobile devices, such as smartphones and tablets integrated with various applications, and their use for traditional purposes such as navigation and geodesy have pushed the development of new techniques and improved algorithms. The combination of multiple techniques is the most common approach. Global Navigation Satellite Systems, such as GPS are well known and established localization systems that have great accuracy but are limited to locations where the strength of the signal is good. Therefore a new approach that could increase the sensitivity and even the accuracy by using GPS could benet a large group of users. This thesis originally proposes a new approach for these signals, based on Particle Filters that proved to work well in mitigating problems in an audio-based localization system. The results obtained show that this approach can increase the sensitivity of the system as a whole and positions estimations can be achieved under conditions that previous systems were not able to.
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Raveendran, Palanivel. "Mechanisms of particle detachment during filter backwashing." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/18989.

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Watson, Paul David Julian. "Geotextile filter design and particle bridge formation." Thesis, Queen Mary, University of London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307520.

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11

AIUBE, FERNANDO ANTONIO LUCENA. "MODELLING COMMODITY FUTURE PRICES: PARTICLE FILTER APPROACH." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7604@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
A evolução dos conhecimentos em Finanças nas últimas três décadas foi rápido e vertiginoso. Hoje os mercados financeiros oferecem produtos sofisticados para investidores e empresas, e por outro lado, tais agentes demandam instrumentos confiáveis para atender suas necessidades em busca de maiores retornos e menores riscos. Todo esse desenvolvimento baseia-se fundamentalmente em metodologias de apreçamento de ativos. Grande parte deste conhecimento é oriundo dos trabalhos pioneiros de Black e Scholes (1973) e Merton (1973). Em síntese, estes trabalhos apoiaram-se em processos estocásticos para preços de ativos para apreçar um derivativo. A natureza do processo estocástico de evolução dos preços é o ponto central para a derivação dos modelos de apreçamento. A análise do comportamento dos preços das commodities possui duas grandes vertentes na literatura. A primeira trata os preços como decorrência de modelos de equilíbrio entre a oferta e a demanda. Estes modelos prosperaram pouco em termos de pesquisa. A outra vertente trata da análise da evolução dos preços baseando-se na série histórica propriamente dita. Esta linha de pesquisa está mais presente na literatura. Esta tese concentra-se nesta abordagem. As commodities possuem características particulares principalmente porque a formação de preços ocorre, via de regra, em mercados futuros. Isto faz com que muitos fatos estilizados não possam ser descritos por modelos de um fator (ou uma variável estocástica). Os fatores (variáveis estocásticas) ou variáveis de estado em muitas situações não são observáveis e necessitam ser estimados. Os modelos de preços futuros, escritos como função das variáveis de estado, recebe o nome de equação de observação. Quando as variáveis de estado são Gaussianas e a equação de observação é linear nos estados, o problema pode ser estimado pelo filtro de Kalman clássico. Se ocorrer a não linearidade, esta dificuldade pode ser contornada pelo filtro de Kalman estendido. Quando o problema é não Gaussiano a literatura usa outras metodologias (freqüentemente aproximações) que não o filtro de Kalman. Esta tese trata de processos estocásticos para preços de commodities propondo extensões aos modelos existentes na literatura. A derivação dos modelos é feita com o uso da transformada de Duffie e Kan (1996) em ambiente de não arbitragem. Algumas das extensões incluem modelos não Gaussianos. Esta tese investiga a estimação destes modelos pela metodologia denominada filtro de partículas. O filtro de partículas é um procedimento recursivo para integração, dentro da classe dos métodos seqüenciais de MonteCarlo. A proposta de utilização desta metodologia decorre do fato de que ela dispensa as condições de linearidade e Gaussianidade. Dentre as contribuições desta tese destacam-se as extensões dos processos estocásticos aplicáveis para quaisquer commodities e as análises de modelos não Gaussianos através da metodologia do filtro de partículas. Além disso, a pesquisa apresenta: (i) conclusões acerca dos modelos de dois fatores aplicados à série de preços da commodity petróleo; (ii) a análise da viabilidade do filtro de partículas mostrando que o erro obtido é próximo daquele do filtro de Kalman para problemas Gaussianos e a resposta obtida da estimação paramétrica é coerente com diversos trabalhos da literatura; (iii) análise da viabilidade operacional de implementação do filtro de partículas em termos do tempo computacional despendido nos processos de filtragem e estimação paramétrica. A tese conclui que o filtro de partículas, apesar ser computacionalmente intenso, é viável na prática face ao imenso desenvolvimento computacional. Ainda mais, por ser uma metodologia aplicável a problemas complexos de inferência, sua utilização em modelos cada vez mais sofisticados é muito promissora.
The evolution of the ideas in Finance has been huge in the last decades. Nowadays the financial markets offer investors sophisticated products. And investors in turn demand reliable financial instruments to meet their needs in search for greater returns and lower risks. This development is based mainly on asset pricing methodologies. The greatest part of this knowledge comes from the seminal works of Black and Scholes (1973) and Merton (1973). To summarize, their works are based on the assumption of a specific stochastic process that governs asset prices. And then a derivative of this underlying asset can be priced. The nature of the stochastic process that describes the evolution of prices is the key point for deriving pricing formulae. The analysis of the behavior of commodity prices has two approaches. The first approach considers prices as a consequence of the equilibrium between supply and demand. These models have not received enough attention in literature. The second approach, which has received more attention, is based on the analysis of price time series. The commodities have particular features because they are most of the times negotiated in future markets. The consequence is that the one factor models badly describe their stylized facts. The factors (stochastic variables) are known as state variables which most of the times are non observables, and need to be estimated. When state variables are Gaussians and the observation equation is linear in states, the classical Kalman filter can be used to access these variables. If non linearity is present extended Kalman filter is used, but when state variables are non Gaussian the literature does not use filtering processes. This thesis analyses the stochastic processes of commodities proposing extensions to the existing models. The derivation of models is based on Duffie and Kan (1996) transform, in a non arbitrage environment. Some extensions are non Gaussian. This thesis investigates the estimation of these models using particle filter methodology. The particle filter is a recursive procedure for integration in the sequential Monte-Carlo methods. The advantage of this methodology is that it does not require linear or Gaussian conditions. The contributions of this research are the extensions of stochastic processes that can be used for any commodity and the use of particle filter as an estimation methodology in Finance. Furthermore the thesis presents: (i) the conclusions about two factor models applied to oil prices; (ii) the analysis of the use of particle filter verifying that errors in both, Kalman filter and particle filter are close and that parameters estimation is in accordance with the literature; (iii) the analysis of the implementation of particle filter showing that it is viable considering the computational time of filtering and parameters estimation. The thesis concludes that the particle filter is viable, although time consuming, due to the hardware development. And more, since particle filter is useful for complex inference problems, its application to sophisticated models is promising.
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Lindmark, Sofia. "Cell Tracking in Microscopy Images Using a Rao-Blackwellized Particle Filter." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-236769.

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Analysing migrating cells in microscopy time-lapse images has already helped the understanding of many biological processes and may be of importance in the development of new medical treatments. Today’s biological experiments tend to produce a huge amount of dynamic image data and tracking the individual cells by hand has become a bottleneck for the further analysis work. A number of cell tracking methods have therefore been developed over the past decades, but still many of the techniques have a limited performance. The aim of this Master Project is to develop a particle filter algorithm that automatically detects and tracks a large number of individual cells in an image sequence. The solution is based on a Rao-Blackwellized particle filter for multiple object tracking. The report also covers a review of existing automatic cell tracking techniques, a review of well-known filter techniques for single target tracking and how these techniques have been developed to handle multiple target tracking. The designed algorithm has been tested on real microscopy image data of neutrophils with 400 to 500 cells in each frame. The designed algorithm works well in areas of the images where no cells touch and can in these situations also correct for some segmentation mistakes. In areas where cells touch, the algorithm works well if the segmentation is correct, but often makes mistakes when it is not. A target effectiveness of 77 percent and a track purity of 80 percent are then achieved.
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Dalin, Magnus, and Stina Måhl. "Radar Distance Positioning System : A Particle Filter Approach." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9475.

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Positioning at sea has been important through all times. Thousands of years ago sea men used the stars to navigate. Today GPS is the most used positioning system at sea. In this thesis an alternative positioning method is described and evaluated. The advantage with the method is that it is independent of external systems which make it harder to interfere with than GPS. By calculating the distance to land using radar echoes (measured from the ship), and compare the distances to a digital sea chart a position can be estimated. There are several problems that have to be solved when using this method. The distance calculation and the comparison with the sea chart result in a non-linear system. One way to handle this non-linearity is the particle filter, which is used in this thesis. When using authentic radar data to estimate a position from an area of 784 km2, the system can isolate a small region around the correct position in two iterations. The system also manages to estimate the position with the same precision as GPS when the ship is moving.

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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 and is shown to perform better for maneuvering targets. Using an approach similar to the acoustic tracker, a radar range-only tracker is also developed. This includes developing the state update and observation models, and proving observability for a batch of range measurements. From an implementation perspective, this thesis provides new low-power and real-time implementations for particle filters. First, to achieve a very low-power implementation, two mixed-mode implementation strategies that use analog and digital components are developed. The mixed-mode implementations use analog, multiple-input translinear element (MITE) networks to realize nonlinear functions. The power dissipated in the mixed-mode implementation of a particle filter-based, bearings-only tracker is compared to a digital implementation that uses the CORDIC algorithm to realize the nonlinear functions. The mixed-mode method that uses predominantly analog components is shown to provide a factor of twenty improvement in power savings compared to a digital implementation. Next, real-time implementation strategies for the batch-based acoustic DOA tracker are developed. The characteristics of the digital implementation of the tracker are quantified using digital signal processor (DSP) and field-programmable gate array (FPGA) implementations. The FPGA implementation uses a soft-core or hard-core processor to implement the Newton search in the particle proposal stage. A MITE implementation of the nonlinear DOA update function in the tracker is also presented.
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Arslan, Ali Erkin. "Range Parameterized Bearings-only Tracking Using Particle Filter." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614888/index.pdf.

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In this study, accurate target tracking for bearings-only tracking problem is investigated. A new tracking filter for this nonlinear problem is designed where both range parameterization and Rao-Blackwellized (marginalized) particle filtering techniques are used in a Gaussian mixture formulation to track both constant velocity and maneuvering targets. The idea of using target turn rate in the state equation in such a way that marginalization is possible is elaborated. Addition to nonlinear nature, unobservability is a major problem of bearings-only tracking. Observer trajectory generation to increase the observability of the bearings-only tracking problem is studied. Novel formulation of observability measures based on mutual information between the state and the measurement sequences are derived for the problem. These measures are used as objective functions to improve observability. Based on the results obtained better understanding of the required observer trajectory for accurate bearings-only target tracking is developed.
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Harris, Peter Richard. "Particle capture from liquid streams by filter papers." Thesis, University of Exeter, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393537.

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Raghuvanshi, Anurag. "Particle filter with Hyperbolic Measurements and Geometry Constraints." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366724596.

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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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Alam, Syed Asad. "Techniques for Efficient Implementation of FIR and Particle Filtering." Doctoral thesis, Linköpings universitet, Datorteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-124195.

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FIR filters occupy a central place many signal processing applications which either alter the shape, frequency or the sampling frequency of the signal. FIR filters are used because of their stability and possibility to have linear-phase but require a high filter order to achieve the same magnitude specifications as compared to IIR filters. Depending on the size of the required transition bandwidth the filter order can range from tens to hundreds to even thousands. Since the implementation of the filters in digital domain requires multipliers and adders, high filter orders translate to a large number of these arithmetic units for its implementation. Research towards reducing the complexity of FIR filters has been going on for decades and the techniques used can be roughly divided into two categories; reduction in the number of multipliers and simplification of the multiplier implementation.  One technique to reduce the number of multipliers is to use cascaded sub-filters with lower complexity to achieve the desired specification, known as FRM. One of the sub-filters is a upsampled model filter whose band edges are an integer multiple, termed as the period L, of the target filter's band edges. Other sub-filters may include complement and masking filters which filter different parts of the spectrum to achieve the desired response. From an implementation point-of-view, time-multiplexing is beneficial because generally the allowable maximum clock frequency supported by the current state-of-the-art semiconductor technology does not correspond to the application bound sample rate. A combination of these two techniques plays a significant role towards efficient implementation of FIR filters. Part of the work presented in this dissertation is architectures for time-multiplexed FRM filters that benefit from the inherent sparsity of the periodic model filters. These time-multiplexed FRM filters not only reduce the number of multipliers but lowers the memory usage. Although the FRM technique requires a higher number delay elements, it results in fewer memories and more energy efficient memory schemes when time-multiplexed. Different memory arrangements and memory access schemes have also been discussed and compared in terms of their efficiency when using both single and dual-port memories. An efficient pipelining scheme has been proposed which reduces the number of pipelining registers while achieving similar clock frequencies. The single optimal point where the number of multiplications is minimum for non-time-multiplexed FRM filters is shown to become a function of both the period, L and time-multiplexing factor, M. This means that the minimum number of multipliers does not always correspond to the minimum number of multiplications which also increases the flexibility of implementation. These filters are shown to achieve power reduction between 23% and 68% for the considered examples. To simplify the multiplier, alternate number systems like the LNS have been used to implement FIR filters, which reduces the multiplications to additions. FIR filters are realized by directly designing them using ILP in the LNS domain in the minimax sense using finite word length constraints. The branch and bound algorithm, a typical algorithm to implement ILP problems, is implemented based on LNS integers and several branching strategies are proposed and evaluated. The filter coefficients thus obtained are compared with the traditional finite word length coefficients obtained in the linear domain. It is shown that LNS FIR filters provide a better approximation  error compared to a standard FIR filter for a given coefficient word length. FIR filters also offer an opportunity in complexity reduction by implementing the multipliers using Booth or standard high-radix multiplication. Both of these multiplication schemes generate pre-computed multiples of the multiplicand which are then selected based on the encoded bits of the multiplier. In TDF FIR filters, one input data is multiplied with a number of coefficients and complexity can be reduced by sharing the pre-computation of the multiplies of the input data for all multiplications. Part of this work includes a systematic and unified approach to the design of such computation sharing multipliers and a comparison of the two forms of multiplication. It also gives closed form expressions for the cost of different parts of multiplication and gives an overview of various ways to implement the select unit with respect to the design of multiplexers. Particle filters are used to solve problems that require estimation of a system. Improved resampling schemes for reducing the latency of the resampling stage is proposed which uses a pre-fetch technique to reduce the latency between 50% to 95%  dependent on the number of pre-fetches. Generalized division-free architectures and compact memory structures are also proposed that map to different resampling algorithms and also help in reducing the complexity of the multinomial resampling algorithm and reduce the number of memories required by up to 50%.
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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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Hektor, Tomas. "Marginalized Particle Filter for Aircraft Navigation in 3-D." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10193.

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In this thesis Sequential Monte Carlo filters, or particle filters, applied to aircraft navigation is considered. This report consists of two parts. The first part is an illustration of the theory behind this thesis project. The second and most important part evaluates the algorithm by using real flight data.

Navigation is about determining one's own position, orientation and velocity. The sensor fusion studied combines data from an inertial navigation system (INS) with measurements of the ground elevation below in order to form a terrain aided positioning system (TAP). The ground elevation measurements are compared with a height database. The height database is highly non-linear, which is why a marginalized particle filter (MPF) is used for the sensor fusion.

Tests have shown that the MPF delivers a stable and good estimate of the position, as long as it receives good data. A comparison with Saab's NINS algorithm showed that the two algorithms perform quite similar, although NINS performs better when data is lacking.

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Yamashita, Hiroshi, Shingo Satake, and Kazuhiro Yamamoto. "Microstructure and particle-laden flow in diesel particulate filter." Elsevier, 2009. http://hdl.handle.net/2237/20047.

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Hosseini, Seyed Alireza. "MODELING PARTICLE FILTRATION AND CAKING IN FIBROUS FILTER MEDIA." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2530.

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This study is aimed at developing modeling methodologies for simulating the flow of air and aerosol particles through fibrous filter media made up of micro- or nano-fibers. The study also deals with modeling particle deposition (due to Brownian diffusion, interception, and inertial impaction) and particle cake formation, on or inside fibrous filters. By computing the air flow field and the trajectory of airborne particles in 3-D virtual geometries that resemble the internal microstructure of fibrous filter media, pressure drop and collection efficiency of micro- or nano-fiber filters are simulated and compared with the available experimental studies. It was demonstrated that the simulations conducted in 3-D disordered fibrous domains, unlike previously reported 2-D cell-model simulations, do not need any empirical correction factors to closely predict experimental observations. This study also reports on the importance of fibers’ cross-sectional shape for filters operating in slip (nano-fiber filters) and no-slip (micro-fiber filters) flow regimes. In particular, it was found that the more streamlined the fiber geometry, the lower the fiber drag caused by a nanofiber relative to that generated by its micron-sized counterpart. This work also presents a methodology for simulating pressure drop and collection efficiency of a filter medium during instantaneous particle loading using the Fluent CFD code, enhanced by using a series of in-house subroutines. These subroutines are developed to allow one to track particles of different sizes, and simulate the formation of 2-D and 3-D dendrite particle deposits in the presence of aerodynamic slip on the surface of the fibers. The deposition of particles on a fiber and the previously deposited particles is made possible by developing additional subroutines, which mark the cells located at the deposition sites and modify their properties to so that they resemble solid or porous particles. Our unsteady-state simulations, in qualitative agreement with the experimental observations reported in the literature, predict the rate of increase of pressure drop and collection efficiency of a filter medium as a function of the mass of the loaded particles.
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Mlích, Jozef. "Sledování objektů ve videosekvencích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235922.

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In this master thesis, image processing methods and methods for statistical modeling of motion are presented. First, description methods of image processing, such as background subtraction method used for object detection, are presented. Next, description of morphological operations, such as dilatation and erosion, is done. Finally, methods for statistical modeling, such as Kalman filter and particle filters, are shown.
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25

Losie, Philip M. "Detection and Tracking of Stealthy Targets Using Particle Filters." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/227.

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In recent years, the particle filter has gained prominence in the area of target tracking because it is robust to non-linear target motion and non-Gaussian additive noise. Traditional track filters, such as the Kalman filter, have been well studied for linear tracking applications, but perform poorly for non-linear applications. The particle filter has been shown to perform well in non-linear applications. The particle filter method is computationally intensive and advances in processor speed and computational power have allowed this method to be implemented in real-time tracking applications. This thesis explores the use of particle filters to detect and track stealthy targets in noisy imagery. Simulated point targets are applied to noisy image data to create an image sequence. A particle filter method known as Track-Before-Detect is developed and used to provide detection and position tracking estimates of a single target as it moves in the image sequence. This method is then extended to track multiple moving targets. The method is analyzed to determine its performance for targets of varying signal-to-noise ratio and for varying particle set sizes. The simulation results show that the Track-Before-Detect method offers a reliable solution for tracking stealthy targets in noisy imagery. The analysis shows that the proper selection of particle set size and algorithm improvements will yield a filter that can track targets in low signal-to-noise environments. The multi-target simulation results show that the method can be extended successfully to multi-target tracking applications. This thesis is a continuation of automatic target recognition and target tracking research at Cal Poly under Dr. John Saghri and is sponsored by Raytheon Space and Airborne Systems.
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Varas, González David. "Region-based particle filter leveraged with a hierarchical co-clustering." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/404443.

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In this thesis, we exploit the hierarchical information associated with images to tackle two fundamental problems of computer vision: video object segmentation and video segmentation. In the first part of the thesis, we present a video object segmentation approach that extends the well knonw particle filter algorithm to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a reformulation of the particle filter theory. We define particles as unions of regions in the current image partition and their propagation is computed through a single optimization process. During this propoagation, the prediction step is performed using a co-clustering between the previous image object partition and a partition of the current one, which allows us to tackle the evolution of non-rigid structures. The second part of the thesis is devoted to the exploration of a co-clustering technique for video segmentation. This technique, given a collection of images and their associated hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes effective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations. Finally, in the last part of the thesis we validate the presented techniques for object and video segmentation using the proposed algorithms as tools to tackle problems in a context for which they were not initially thought.
En aquesta tesi, utilitzem la informació jeràrquica associada a les imatges per explorar dos problemes fonamentals de la visió per ordinador: segmentació d'objectes en vídeos i segmentació de vídeos. A la primera part de la tesi, presentem un enfoc per a la segmentació d'objectes en vídeos que estén l'algoritme de filtres de partícules a una representació basada en regions de la imatge. La partició de la imatge es considera part de la mesura del filtre de partícules, enriquint la informació disponible i permetent una reformulació de la teoria dels filtres de partícules. Definim les partícules com a unions de regions de la partició i la propagació es calcula utilitzant únicament un procés d'optimització. En aquesta propagació, la etapa de predicció es realitza mitjançant un co-clustering entres la partició de l'objecte a l'instant de temps anterior i la partició actual, permetent el seguiment d'estructures no rígides. La segona part de la tesi està dedicada al desenvolupament d'una tècnica de co-clustering per segmentació de vídeo. Donada una col·lecció d'imatges i les seves jerarquies associades, aquesta tècnica agrupa nodes de les jerarquies per obtenir una representació de la col·lecció d'imatges coherent en diferents resolucions. Formalitzem el co-clustering com un problema d'optimització linial i el resolem utilitzant unes restriccions que permeten utilitzar de manera efectiva la informació de les jerarquies. Inicialment, afrontem el problema generant una partició òptima coherent per imatge per, posteriorment, estendre aquest mètode a un context de multi-resolució. Finalment, particularitzem aquesta tècnica com a algoritme iteratiu de segmentació de vídeo a diferents resolucions en seqüències amb poc moviment. Finalment, a la darrera part de la tesi, validem les tècniques presentades per segmentació d'objectes en vídeo i segmentació de vídeo utilitzant els algoritmes proposats com a eines per resoldre problemes pels quals no havien estat pensats inicialment.
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27

Ludington, Ben T. "Particle filter tracking architecture for use onboard unmanned aerial vehilces." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11142006-152845/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007.
Vachtsevanos, George, Committee Chair ; Heck, Bonnie, Committee Member ; Vela, Patricio, Committee Member ; Yezi, Anthony, Committee Member ; Johnson, Eric, Committee Member.
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28

Ludington, Ben T. "Particle Filter Tracking Architecture for use Onboard Unmanned Aerial Vehicles." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/13967.

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Unmanned Aerial Vehicles (UAVs) are capable of placing sensors at unique vantage points without endangering a pilot. Therefore, they are well suited to perform target tracking missions. However, performing the mission can be burdensome for the operator. To track a target, the operator must estimate the position of the target from the incoming video stream, update the orientation of the camera, and move the vehicle to an appropriate vantage point. The purpose of the research in this thesis is to provide a target tracking system that performs these tasks automatically in real-time. The first task, which receives the majority of the attention, is estimating the position of the target within the incoming video stream. Because of the inherent clutter in the imagery, the resulting probability distributions are typically non-Gaussian and multi-modal. Therefore, classical state estimation techniques, such as the Kalman filter and its variants are unacceptable solutions. The particle filter has become a popular alternative since it is able to approximate the multi-modal distributions using a set of samples, and it is used as part of this research. To improve the performance of the filter and manage the inherently large computational burden a neural network is used to estimate the performance of the particle filter. The filter parameters are then changed in response to the performance. Once the position of the target is estimated in the frame, it is projected on the ground using the camera orientation and vehicle attitude and input into a linear predictor. The output of the predictor is used to update the orientation of the camera and vehicle waypoints. Through offline, simulation, and flight testing, the approach is shown to provide a powerful visual tracking system for use onboard the GTMax unmanned research helicopter.
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29

Eriksson, Simon. "Map-aided localization for autonomous driving using a particle filter." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280812.

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Vehicles losing their GPS signal is a considerable issue for autonomous vehicles and can be a danger to people in their vicinity. To circumvent this issue, a particle filter localization technique using pre-generated offline Open Street Map (OSM) maps was investigated in a software simulation of Scania’s heavy-duty trucks. The localization technique runs in real-time and provides a way to localize the vehicle safely if the starting position is known. Access to global localization was limited, and the particle filter still succeeded in localizing the vehicle in the vicinity of the correct road segment by creating a graph of the map information and matching the trajectory to the vehicle’s sensor data. The mean error of the Particle filter localization technique in optimal conditions is 16m, which is 20% less than an optimally tuned dead reckoning solution. The mean error is about 50% larger compared to a Global Positioning System. The final product shows potential for expansion but requires more investigation to allow for real-world deployment.
Att fordon kan mista sin GPS-signal är ett stort problem för autonoma fordon och kan vara en fara för människor i dess närhet. För att undvika detta problem föreslås en icke-global lokaliseringsteknik som använder Open Street Maps-kartor (OSM) och ett partikelfilter för att lokalisera fordonet i en mjukvarusimulation. Implementering körs i realtid och anger fordonets position med en tillräcklig träffsäkerhet för att det inte ska utgöra någon fara om dess startposition är känd. Globala lokaliseringsmöjligheter var begränsade, och partikelfiltret lyckades lokalisera fordonet till rätt vägsegment genom att konstruera en graf över den kartinformation den läst in och para ihop fordonets nuvarande färdväg med denna. Resultatet ger en lösning som optimalt har ett medelfel på 16m, vilket är 20% mindre än medelfelet jämfört med optimiserad dödräkning. Lösningen har ca 50% större medelfel än positionering med GPS. Slutresultatet visar en potential att användas i verkliga situationer, men kräver mer undersökningar.
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30

Manuelli, Lucas Ph D. Massachusetts Institute of Technology. "Localizing external contact using proprioceptive sensors : the contact particle filter." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115739.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-65).
In order for robots to interact safely and intelligently with their environment they must be able to reliably estimate and localize external contacts. 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 returns sensible estimates even when the external torque measurement is corrupted with noise. We demonstrate the capability of the CPF in multiple scenarios. We show how it can track multiple external contacts on a simulated Atlas robot, and also perform extensive simulation and hardware experiments on a Kuka iiwa robot arm.
by Lucas Manuelli.
S.M.
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31

Frykman, Petter. "Applied particle filters in integrated aircraft navigation." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1736.

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Navigation is about knowing your own position, orientation and velocity relative to some geographic entities. The sensor fusion considered in this thesis combines data from a dead reckoning system, inertial navigation system (INS), and measurements of the ground elevation. The very fast dynamics of aircraft navigation makes it difficult to estimate the true states. Instead the algorithm studied will estimate the errors of the INS and compensate for them. A height database is used along with the measurements. The height database is highly non-linear why a Rao-Blackwellized particle filter is used for the sensor fusion. This integrated navigation system only uses data from its own sensors and from the height database, which means that it is independent of information from outside the aircraft.

This report will describe the algorithm and illustrate the theory used. The main purpose is to evaluate the algorithm using real flight data, why the result chapter is the most important.

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32

Aghakarimi, Armin. "Local Modeling Of The Ionospheric Vertical Total Electron Content (vtec) Using Particle Filter." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614867/index.pdf.

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ABSTRACT LOCAL MODELING OF THE IONOSPHERIC VERTICAL TOTAL ELECTRON CONTENT (VTEC) USING PARTICLE FILTER Aghakarimi, Armin M.Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Prof. Dr. Mahmut Onur Karslioglu September 2012, 98 pages Ionosphere modeling is an important field of current studies because of its influences on the propagation of the electromagnetic signals. Among the various methods of obtaining ionospheric information, Global Positioning System (GPS) is the most prominent one because of extensive stations distributed all over the world. There are several studies in the literature related to the modeling of the ionosphere in terms of Total Electron Content (TEC). However, most of these studies investigate the ionosphere in the global and regional scales. On the other hand, complex dynamic of the ionosphere requires further studies in the local structure of the TEC distribution. In this work, Particle filter has been used for the investigation of local character of the ionosphere VTEC. Besides, standard Kalman filter as an effective method for optimal state estimation is applied to the same data sets to compare the corresponding results with results of Particle filter. The comparison shows that Particle filter indicates better performance than the standard Kalman filter especially during the geomagnetic storm. MATLAB©
R2011 software has been used for programing all processes and algorithms of the study.
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33

Vafai, Fereydoon. "Analytical modelling and laboratory studies of particle transport in filter media." Online version, 1996. http://bibpurl.oclc.org/web/23534.

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34

Eng, Donald S. "State estimation for a holonomic omniwheel robot using a particle filter." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61159.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 62).
The holonomic robot platform designed for the Opera of the Future must perform continuously on stage in a 10 meter by 20 meter world for one hour. The robot interacts with twelve other robots, stage elements, and human performers. Fast, accurate, and continuous state estimation for robot pose is a critical component for robots to safely perform on stage in front of a live audience. 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 confounds the Ubisense measurement of 0, but the Particle Filter acts as a low pass filter on the absolute positions and mixes the high frequency components of the odometry to determine an accurate estimate of the robot pose.
by Donald S. Eng.
M.Eng.
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35

Li, Yi-Lun, and 李翊綸. "Hand Tracking Using Particle Filter." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/22763283377365156879.

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碩士
華梵大學
資訊管理學系碩士班
96
In this thesis, a system for tracking hands without markers in complex scenes has been developed. Study of particle filters and CONDENSATION, and for the tracking system in hand . Particle filter is used to predict the center position of the hand, the hand template matching is used to track a hand, and the CONDENSATION (CONditional DENSity PropagATION) is used to track the contour of hand. In the particle filter. Given the image in the previous time, the color histogram of an image patch in the center position of the hand can be obtained. After the spread of the particles in the current image, the color histograms between the two image patches in the previous and current images are used as the similarity measurement. Here, the similarity is converted to be the weights, and the expectation is used for predicting the hand center in the next image. In the hand template matching. In generating the hand templates, 36 hand templates are generated for a hand pose (with rotation of 10 degrees for a hand template). According to the edge orientation, each hand template is divided into several channels. Each image has been processed using the following procedures: edge orientation and distance transform (DT). The best hand location can be found by using the hand template matching. In this thesis, we proposed to combine the particle filters with the template matching for hand tracking. Using the particle filter, the possible regions for the hand template matching can be reduced. It means that it can speed up the system. In this thesis, some preliminary experimental results for tracking the contours of a hand in implementing CONDENSATION algorithm.
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YADAV, SUMAN. "OBJECT TRACKING USING PARTICLE FILTER." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14504.

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ABSTRACT In this project, a particle filter is implemented in a color model based framework to track the moving object in outdoor environment. firstly the initialisation of samples is done in first frame by drawing them randomly on the screen or drawing them based on the region where the object is expected to appear. Next the samples are predicted based on system model by propagating each sample based on this model. The samples are updated based on the observation model. In this report, we use color distribution of the object as the observation model. Then using the bhattacharya distance, the similarity between the color distribution of the target & the samples can be measured. Based on the bhattacharya distance weight of each sample is measured. The target state estimation is performed based on samples weight. The resampling is performed for the next sample iteration to generate a new sample set. During the resampling sample with a high weight are chosen leading to identical copies, while others with relatively low weights may be ignored & deleted.
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Choi, Chul Woo. "Peak detection via a particle filter." 2003. http://catalog.hathitrust.org/api/volumes/oclc/52725628.html.

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Thesis (M.S.)--University of Wisconsin--Madison, 2003.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 16).
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Chang, Yen-Hsiang, and 張雁翔. "Occluded Pedestrian Tracking using Collaboration of Kalman Filter and Particle Filter." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/79092032066548672333.

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碩士
國立臺北大學
通訊工程研究所
98
Object tracking is a one of the key feature in intelligent video surveillance. It is a challenging task in tracking algorithm due to the frequent occlusion encountered between moving objects. We propose a novel method to address the problem of tracking and evaluating the number of people in multiple people scenes with an occlusion condition. The proposed method combines an object tracking system and a head detection. In our framework, Kalman Filter and Particle Filter provide robust object tracking for solving the occlusion between moving object. The head detection adopts the color model and shape-based object detection for counting the number of people. Extensive experimental results show that our method possesses effective and efficient performance.
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39

Pin-JieWu and 吳品頡. "Sparse-Based Object Tracking Using Particle Filter." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/36641097921412749884.

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碩士
國立成功大學
資訊工程學系
102
Sparse representation is a significant technique in resent tracking research. However, there are many challenges in the real-world tracking task such as occlusion or appearance change. In this thesis, we propose a sparse-based tracking algorithm with global and local information. We also propose a robust template update scheme to catch the appearance variance. Two kinds of template are updated for global and local information independently. For global information, a stable template set and a normal template set are used to capture the appearance change. The background template set is also considered. For local information, a patches-based dictionary is updated in the tracking task. By the robust template update scheme, we can conquer serious appearance change in the tracking task.
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40

Tang, Chi-Hung, and 湯騏鴻. "Robust Color Histograms for Particle Filter Tracking." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/24696465548686256255.

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碩士
國立清華大學
資訊工程學系
95
Video object tracking is a very important issue in computer vision applications, such as video surveillance, perceptual user interfaces, and object-based video compression. The difficulty of video object tracking might come from many factors, such as cluttered background, occlusions, lighting changes and deformation. In this thesis, we assume that color features in the neighborhood of interest points are important, and propose to use a robust color histogram based on SURF. As shown in the experimental results, the robust color histogram performs well in several difficult scenarios, such as lighting changes and occlusions. Moreover, we use the robust color histogram as the appearance model of the target object to assist video object tracking. We modify the traditional particle filter framework and view each SURF interest point as a particle to develop a new object tracking algorithm. In the experimental results, we have shown that the performance of our object tracking algorithm is good.
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41

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 obtained from both these filters converge to the optimal solution obtained by the Kalman Filter. However, it is of interest to explore how the two filters compare to each other in basic methodology and construction, especially due to the similarity between them. In this work, we take up a specific problem of Bayesian inference in a restricted framework and compare analytically the results obtained from the particle filter and the ensemble Kalman filter. We show that for the chosen model, under certain assumptions, the two filters become methodologically analogous as the sample size goes to infinity.
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42

Marek, Jiří. "SLAM a navigace s použitím RBPF (Rao-Blackwellized Particle Filter)." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-387365.

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This work presents a design of an indoor/outdoor SLAM technique combined with navigation for mobile robots. The system does not use any external beacons and relies on only one 2D range finder. This work focuses mainly on an implementation of already established algorithms which were significantly improved (which in effect helped also to overcome the set sensory limitations). To localize the robot and create a map of an unknown environment, we are using a variant of a Rao-Blackwell's particle filter. We also present techniques for navigating in the map and recognizing terrain types. The method for recognizing terrain types creates a much more unique map and also improves the outdoor localization. The outdoor environment that we focused on are city parks where the robot has to stay on designated paths.
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43

Tan-HoaNguyen and 阮單浩. "Application of the Particle Filter and Extended Kalman Filter in Mobile Robot Localization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87238954930331495718.

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碩士
國立成功大學
電機工程學系碩博士班
99
State estimation is a major problem in mobile robot localization. To this end Gaussian and nonparametric filters have been developed. In this thesis, the extended Kalman filter which assumes Gaussian measurement noise is compared to the particle filter which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of a mobile robot is used and measurements are available from both odometer and ranger sensors. It is shown that in this kind of localization, the particle filter has improved performance and has wider applications than the extended Kalman filter, at the cost of more demanding computations.
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44

Milstein, Adam. "Improved Particle Filter Based Localization and Mapping Techniques." Thesis, 2008. http://hdl.handle.net/10012/3619.

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One of the most fundamental problems in mobile robotics is localization. The solution to most problems requires that the robot first determine its location in the environment. Even if the absolute position is not necessary, the robot must know where it is in relation to other objects. Virtually all activities require this preliminary knowledge. Another part of the localization problem is mapping, the robot’s position depends on its representation of the environment. An object’s position cannot be known in isolation, but must be determined in relation to the other objects. A map gives the robot’s understanding of the world around it, allowing localization to provide a position within that representation. The quality of localization thus depends directly on the quality of mapping. When a robot is moving in an unknown environment these problems must be solved simultaneously in a problem called SLAM (Simultaneous Localization and Mapping). Some of the best current techniques for localization and SLAM are based on particle filters which approximate the belief state. Monte Carlo Localization (MCL) is a solution to basic localization, while FastSLAM is used to solve the SLAM problem. Although these techniques are powerful, certain assumptions reduce their effectiveness. In particular, both techniques assume an underlying static environment, as well as certain basic sensor models. Also, MCL applies to the case where the map is entirely known while FastSLAM solves an entirely unknown map. In the case of partial knowledge, MCL cannot succeed while FastSLAM must discard the additional information. My research provides improvements to particle based localization and mapping which overcome some of the problems with these techniques, without reducing the original capabilities of the algorithms. I also extend their application to additional situations and make them more robust to several types of error. The improved solutions allow more accurate localization to be performed, so that robots can be used in additional situations.
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Liu, Yu-lun, and 劉育倫. "An Improved Head Tracking System Using Particle Filter." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/50343642326136013470.

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碩士
國立中央大學
通訊工程研究所
96
Object tracking is an important technique in computer vision, and it can be applied in applications such as visual surveillance and human-robot interaction. How to estimate object scale accurately and choose proper feature to improve tracking accuracy is an important issue. In this paper, our tracking system tracks human heads with particle filter with non-linear and non-Gaussian state transition and measurement. We integrate head detection into tracking system and propose to start head localization with various features based on color similarity of tracking measurement. We reset target color histogram and head scale if needed. Experimental results show that our head tracking system has good tracking accuracy under human regular motion, fast motion and distance variation between the target and the camera.
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Chiang, Yu-Ting, and 蔣瑜婷. "Object Tracking Using Particle Filter with SURF Feature." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/10212647227316650770.

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碩士
國立東華大學
資訊工程學系
101
Object tracking is important in many applications in computer vision, e.g., video analysis, intelligent vehicle, surveillance system, robot vision, human-computer interaction, and so on. This topic has received much attention in the recent decade. Although the topic of object tracking has been well studied in computer vision, it still remains challenge in varying illumination condition, noise influence, scene change, clutter background, occlusion, and similar color. Therefore, how to develop a robust method for object tracking is seriously important. In this thesis, a novel object tracking based on particle filter and SURF feature is proposed. The proposed method uses not only color feature but also SURF feature. The SURF feature makes the tracking result more robust. Particle selection can lead to time saving. In addition, we also consider the matched particle applicable to calculating SURF weight. Owing to the color, spatial, and SURF features being adopted, this method is more robust than traditional color-based appearance model. Experimental results demonstrate the robustness and accurate tracking results with challenging sequences. Besides, the proposed method outperforms other methods during intersection of similar color and object’s partial occlusion.
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47

Zhou, Zi-Jie, and 周子傑. "Characteristics and Performance Evaluation of Airborne Particle Filter." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n76s72.

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碩士
國立臺北科技大學
環境工程與管理研究所
107
This study evaluated the characteristics and filtration performance of common filter materials Glass fiber, polytetrafluoroethylene, polypropylene, and two commercially available polypropylene filters. Analyze the surface characteristics, charge, and packing density of the filter material. The fiber diameter and pore size have a corresponding relationship: PTFE
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48

Cui, Jing. "In vivo multi-leukocyte tracking by enhanced particle filter /." 2006. http://wwwlib.umi.com/dissertations/fullcit/3225920.

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49

Leung, Keith Yu Kit. "Monocular Vision based Particle Filter Localization in Urban Environments." Thesis, 2007. http://hdl.handle.net/10012/3321.

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This thesis presents the design and experimental result of a monocular vision based particle filter localization system for urban settings that uses aerial orthoimagery as a reference map. The topics of perception and localization are reviewed along with their modeling using a probabilistic framework. Computer vision techniques used to create the feature map and to extract features from camera images are discussed. Localization results indicate that the design is viable.
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50

Lee, Li-Yin, and 李立尹. "Object Tracking Based on Adaboost Classifier and Particle Filter." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/42225030461804640292.

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Abstract:
碩士
亞東技術學院
資訊與通訊工程研究所
101
Application of object tracking has always been an important issue in computer vision or image processing applications. In the early stages, object tracking had been applied to air traffic control. Recently, it has often been applied to with security monitoring related fields. There are various types of methods for object tracking. Generally, these methods can be divided into time domain methods and space domain methods. In a time domain system, the target must be able to show time differences. In other words, the target has to move so that judgments can be made. In a space domain system, judgments are made based on image characteristics of the targets. And usually judging methods based on characteristic information are more complex and diversified. However, if only a time domain method is applied, the only thing that can be confirmed is that whether the target is moving or not. It is difficult to find out if this target is the interest one. On the other hand, particle filter is a good solution for target tracking but is only applicable when the scene is known and possible locations of the target are preset. However, adding the adaboost algorithm helps to solve this issue. Therefore, this study proposed a combined structure with adaboost detection and particle filtering method to resolve the problems mentioned above for the pedestrian tracking problems. Considering this problem, a hybrid structure combining adaboost classifier and particle filter is proposed to automatically detect and track the pedestrian targets in this paper. The adaboost detection process is adopted first to target candidate objects, and then the particle filter is applied for confirming and tracking of targets. Experiment results show that via the proposed method, the drawback of the current particle filters which requires specifying an object to be tracked in advance can be overcome, while performing good also in cases of target missing, occlusion, and identifying the previously appeared objects. Like other current methods, the issue of bad performances in detection and tracking in a complex environment still exists with the object tracking method proposed by this study. In the future, we will add a preprocessing step of image enhancement before target detection and increase the number of negative samples in the sample for cascade training, to solve the issue above, so this system can be applied more widely with efficiency.
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