1,792,761 research outputs found
A parallel Kalman filter via the square root Kalman filtering
Get PDFA parallel algorithm for Kalman filtering with contaminated observations is developed. Theı parallel implementation is based on the square root version of the Kalman filter (see [3]). Thisı represents a great improvement over serial implementations reducing drastically computationalı costs for each state update
Kalman Filtering in R
Get PDFSupport 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.
Get PDFThe 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.
Square root kalman filter with contaminated observations
Get PDFThe 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
Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation
Get PDFModeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the proces
On credibility and robustness with the Kalman filter
Get PDFBühlmann (1967) gave a formal Bayesian derivation of the credibility ratio estimators that actuaries had been using for many years. Since then various generalizations of Bühlmann's model have appeared in the literature, each relaxing the i.i.d. assumptions in its own way. The introduction of weights is due to Bülhmann & Straub (1970) and that the regressors to Hachemeister (1975), but the first comprehensive actuarial application of the Kalman filter is due to de Jong & Zehnwirth (1983). More recent efforts have concentrated on the robustification of these estimators, as they provedı to be extremely sensitive to large claims. Kremer (1991) studies a robust regression credibility model and Künsch (1992) tackles the weighted case. Following Kremer (1994) we propose here a robust Kalman filter credibility model
Stochastic Surface Models for Commodity Futures: A 2D Kalman Filter Approach
Get PDFWe 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
Process fault prognosis using a fuzzy-adaptive unscented Kalman predictor
Get PDFBy 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].
Get PDFSejak 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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