177,481 research outputs found
An unscented Kalman filter for freeway traffic estimation
No full textThis paper addresses the problem of freeway tra±c flow estimation. The freeway is considered as a network of components representing different freeway stretches called segments. The evolution of the traffic in a segment is modelled as a dynamic stochastic system, influenced by states of neighbour segments. Measurements are received only at boundaries between some segments and averaged within regular time intervals. An Unscented Kalman filter is developed and its performance is compared with a particle filter both for synthetic data and for real traffc data. The intended application is to supply traffc control systems with the estimated traffc state
Kalman Filtering in R
No full textSupport in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help the prospective user to make an informed choice.
Understanding the Kalman Filter: an Object Oriented Programming Perspective.
No full textThe basic ideals underlying the Kalman filter are outlined in this paper without direct recourse to the complex formulae normally associated with this method. The novel feature of the paper is its reliance on a new algebraic system based on the first two moments of the multivariate normal distribution. The resulting framework lends itself to an object-oriented implementation on computing machines and so many of the ideas are presented in these terms. The paper provides yet another perspective of Kalman filtering, one that many should find relatively easy to understand.Time series analysis, forecasting, Kalman filter, dynamic linear statistical models, object oriented programming.
Estimação de tempos de chegada de ônibus urbano utilizando filtros de Kalman
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Automação e Sistemas.A fim de diminuir congestionamentos, poluição do ar, consumo de combustível, entre outros, tem-se buscado constantemente o desenvolvimento e modernização de sistemas de transporte público, pois sistemas mais eficientes, confortáveis e convenientes atraem um maior número de pessoas. A aplicação de linhas de ônibus em ambientes urbanos, tem sido um dos modos de transporte público mais utilizados. Para operação eficiente destas linhas, é importante conhecer a posição do veículo em tempo real, possibilitando o controle dos instantes de partida e a implantação de sistemas de informação sobre chegadas futuras, melhorando a percepção de qualidade do serviço prestado. A predição dos tempos de chegada do ônibus depende de uma série de fatores (por exemplo, atrasos em interseções sinalizadas, número de passageiros em pontos de parada, etc.). Estes fatores aumentam significativamente o nível das incertezas associadas ao processo e à medição. Este trabalho apresenta um algoritmo para predição dos tempos de chegada de ônibus urbano em pontos de parada, utilizando a abordagem de filtro de Kalman com análise de dados históricos. Fatores que aumentam o nível das incertezas associadas ao processo e à medição são considerados como propriedades estocásticas das perturbações do processo. A geração de dados de medição é realizada através de dois cenários distintos desenvolvidos e simulados no software de simulações microscópicas Aimsun 6.1. O teste de ajustamento de Kolmogorov-Smirnov é aplicado para análise das distribuições estatísticas destes dados. Os parâmetros necessários para configuração do filtro de Kalman são obtidos a partir de dados históricos através de dois métodos de análise estatística propostos: o método de análise longitudinal e o método de análise transversal. O filtro de Kalman é utilizado para estimação de dois estados do veículo, sua posição e sua velocidade. Por fim, é proposto um algoritmo que utiliza as estimações oriundas do filtro de Kalman para realizar a predição dos tempos de chegada do ônibus em pontos de parada.In order to reduce traffic congestions, air pollution, fuel consumption, and others, it has been constantly sought the development and modernization of public transportation systems, because more efficient, comfortable, and convenient systems attract more people. The application of bus lines in urban environments has been one of the public transportation modes most used. However, for efficient operation of bus lines, its important to know the vehicle position in real-time, enabling the control of departure times and implantation of information systems about future arrivals. The prediction of bus arrival time depends on a number of factors (e.g., delays at signalized intersections, number of passengers at bus stops, etc.). These factors increase significantly the level of uncertainties associated to process and measurement. This work presents an algorithm for prediction of bus arrival times at bus stops using Kalman filter with historical data analysis approach. Factors that increase the level of uncertainties associated to process and measurement are considerate stochastic properties of process disturbance. The generating of measurement data is performed by two different scenarios developed and simulated on the microscopic simulation software Aimsun 6.1. The Kolmogorov-Smirnov goodness of fit test is applied for analysis of the statistical distributions of these data. The parameters required for configuration of the Kalman filter are obtained from historical data through two proposed methods of statistical analysis: the method of longitudinal analysis, and the method of transversal analysis. The Kalman filter is used for estimation of two vehicle states, its position and its velocity. Finally, an algorithm is proposed that uses the estimates given by the Kalman filter to perform the prediction of bus arrival times at bus stops
Square root kalman filter with contaminated observations
No full textThe algorithm of square root Kalman filtering for the case of contaminated observations is described in the paper. This algorithm is suitable for the parallel computer implementation allowing to treat dynamic linear systems with large number of state variables in a robust recursive way
Stochastic Surface Models for Commodity Futures: A 2D Kalman Filter Approach
No full textWe propose a two-dimensional Kalman filter approach that, additional to the information contained in futures prices evolution over time, makes use of information contained in the term structure of commodity futures along a second dimension of maturities. This time-maturity surface reflects a complete realization of the stochastic process as an alternative to standard Kalman filtering of a limited vector of futures prices along the one-dimensional time line. Thus, the proposed methodology may use the full information from the entire surface dynamics, including links from all available maturities per period, which eventually should lead to more accurate model parameter estimates. The technique is illustrated using coal futures prices.commodity prices, spatial analysis, two-dimensional Kalman filter, energy markets, futures markets, stochastic dynamic model
State estimation in chemometrics : the Kalman filter and beyond /
No full textThis unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Includes bibliographical references and index.Print version record.This unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Cover; ABOUT OUR AUTHOR; STATE ESTIMATION IN CHEMOMETRICS: The Kalman Filter and Beyond; Copyright; Contents; 1 Introduction; 1.1 History; 1.2 Chemometrics; 1.3 System view; 2 Classical estimation; 2.1 Linear model; 2.2 Least squares; 2.3 Curve fitting; 2.4 Recursive approach; 2.5 Examples; 3 State estimation; 3.1 State space model; 3.2 Intermezzo; 3.3 Prediction; 3.4 Filtering; 3.5 Kalman filter; 3.6 Smoothing; 3.7 Examples; 4 Statistics; 4.1 Verification; 4.2 Evaluation; 4.3 Selection; 4.4 Normality; 4.5 Example; 5 Non-linear estimation; 5.1 Non-linear state space model.5.2 Extended Kalman filter5. 3 Iterated extended Kalman filter; 5.4 Iterated linear filter-smoother; 5.5 Non-linear smoothing; 5.6 Examples; 6 The multicomponent system; 6.1 Multicomponent analysis; 6.2 Stochastic drift; 6.3 Examples; 7 The calibration system; 7.1 Linear calibration; 7.2 Non-linear calibration; 7.3 Examples; 8 The titration system; 8.1 Discrete titration; 8.2 Continuous titration; 8.3 Non-linear model; 8.4 Examples; 9 Miscellaneous; 9.1 Multiple modeling; 9.2 Principal components; 9.3 Examples; Appendix; Matrix fundamentals; Bibliography; Index.Elsevie
Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor
No full textBy monitoring the future process status via information prediction, process fault prognosis is able to give an early alarm and therefore prevent faults, when the faults are still in their early stages. A fuzzy-adaptive unscented Kalman filter (FAUKF)-based predictor is proposed to improve the tracking and forecasting capability for process fault prognosis. The predictor combines the strong tracking concept and fuzzy logic idea. Similar to the standard adaptive unscented Kalman filter (AUKF) that employs an adaptive parameter to correct the estimation error covariance, a Takagi–Sugeno fuzzy logic system is designed to provide a better adaptive parameter for smoothing this regulation. Compared with the standard AUKF, the proposed FAUKF has the same strong tracking ability but does not suffer from the drawback of serious tracking fluctuation. Two simulation examples demonstrate the effectiveness of the proposed predictor
Dynamics Between Malaysian Equity Market And Macroeconomic Variables : An Application Of Kalman Filter Model With Heteroskedastic Error [QA402.3. C514 2007 f rb].
No full textSejak diperkenalkan oleh Kalman dan Bucy (1960), model penapis Kalman telah mendapat penggunaan yang luas dalam dalam program ruang angkasa dan bidang kejuteraan kawalan. Namun begitu, pengaplikasiannya dalam bidang siri masa kewangan masih jarang digunakan dan jauh ketinggalan.
Ever since the pioneering work of Kalman and Bucy (1960), Kalman filter model has become widely used in the space programme and control engineering. However, its applications in financial time series have been very few and far in between
On Kalman Filtering with Nonlinear Equality Constraints
No full textThe state space description of some physical systems possess nonlinear equality constraints between some state variables. In this paper, we consider the problem of applying a Kalman filter-type estimator in the presence of such constraints. We categorize previous approaches into pseudo-observation and projection methods and identify two types of constraints-those that act on the entire distribution and those that act on the mean of the distribution. We argue that the pseudo-observation approach enforces neither type of constraint and that the projection method enforces the first type of constraint only. We propose a new method that utilizes the projection method twice-once to constrain the entire distribution and once to constrain the statistics of the distribution. We illustrate these algorithms in a tracking system that uses unit quaternions to encode orientation
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