			Home About us Contact

Filter Algorithm (filter + algorithm)

Distribution by Scientific Domains

Engineering	40%

Selected Abstracts

Spatio-temporal point process filtering methods with an application

ENVIRONMETRICS, Issue 3-4 2010
ena Frcalová
Abstract The paper deals with point processes in space and time and the problem of filtering. Real data monitoring the spiking activity of a place cell of hippocampus of a rat moving in an environment are evaluated. Two approaches to the modelling and methodology are discussed. The first one (known from literature) is based on recursive equations which enable to describe an adaptive system. Sequential Monte Carlo methods including particle filter algorithm are available for the solution. The second approach makes use of a continuous time shot-noise Cox point process model. The inference of the driving intensity leads to a nonlinear filtering problem. Parametric models support the solution by means of the Bayesian Markov chain Monte Carlo methods, moreover the Cox model enables to detect adaptivness. Model selection is discussed, numerical results are presented and interpreted. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 5 2007
Nikolas Trawny
In this paper we describe an extended Kalman filter algorithm for estimating the pose and velocity of a spacecraft during entry, descent, and landing. The proposed estimator combines measurements of rotational velocity and acceleration from an inertial measurement unit (IMU) with observations of a priori mapped landmarks, such as craters or other visual features, that exist on the surface of a planet. The tight coupling of inertial sensory information with visual cues results in accurate, robust state estimates available at a high bandwidth. The dimensions of the landing uncertainty ellipses achieved by the proposed algorithm are three orders of magnitude smaller than those possible when relying exclusively on IMU integration. Extensive experimental and simulation results are presented, which demonstrate the applicability of the algorithm on real-world data and analyze the dependence of its accuracy on several system design parameters. © 2007 Wiley Periodicals, Inc. [source]

A New-Keynesian DSGE model for forecasting the South African economy

JOURNAL OF FORECASTING, Issue 5 2009
Dave' Liu, Guangling
Abstract This paper develops a New-Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model for forecasting the growth rate of output, inflation, and the nominal short-term interest rate (91 days Treasury Bill rate) for the South African economy. The model is estimated via maximum likelihood technique for quarterly data over the period of 1970:1,2000:4. Based on a recursive estimation using the Kalman filter algorithm, out-of-sample forecasts from the NKDSGE model are compared with forecasts generated from the classical and Bayesian variants of vector autoregression (VAR) models for the period 2001:1,2006:4. The results indicate that in terms of out-of-sample forecasting, the NKDSGE model outperforms both the classical and Bayesian VARs for inflation, but not for output growth and nominal short-term interest rate. However, differences in RMSEs are not significant across the models. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Stochastic mixed integer nonlinear programming using rank filter and ordinal optimization

AICHE JOURNAL, Issue 11 2009
Chengtao Wen
Abstract A rank filter algorithm is developed to cope with the computational-difficulty in solving stochastic mixed integer nonlinear programming (SMINLP) problems. The proposed approximation method estimates the expected performance values, whose relative rank forms a subset of good solutions with high probability. Suboptimal solutions are obtained by searching the subset using the accurate performances. High-computational efficiency is achieved, because the accurate performance is limited to a small subset of the search space. Three benchmark problems show that the rank filter algorithm can reduce computational expense by several orders of magnitude without significant loss of precision. The rank filter algorithm presents an efficient approach for solving the large-scale SMINLP problems that are nonconvex, highly combinatorial, and strongly nonlinear. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

Robust unscented Kalman filtering for nonlinear uncertain systems

ASIAN JOURNAL OF CONTROL, Issue 3 2010
K. Xiong
Abstract A derivative-free robust Kalman filter algorithm is proposed for nonlinear uncertain systems. The unscented transform (UT) is adopted instead of the linearization technique to obtain the solution of the H, filter Riccati equation. A robust unscented Kalman filter (RUKF) is derived to guarantee an optimized upper bound on the estimation error covariance despite the model uncertainties and the approximation error of the UT. The proposed algorithm is applied to a satellite attitude determination system. Simulation results show that the RUKF is more effective than the unscented Kalman filter (UKF) in cases where alignment errors are present. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]