Search results for "Kalman Filter"

Interest rate gaps in an uncertain global context: why "too" low (high) for "so" long?

2022

We study the behaviour of real interest rate gaps-i.e. periods of real interest rates above (below) the natural interest rate-and link their length with a set of key observable determinants. Using quarterly data for 13 OECD countries over (close to) the last 60 years, we find that global risk-taking, CPI inflation, (un)conventional monetary policy, and income redistribution crucially shape the duration of both events. However, while labour-related supply-side factors appear to affect the length of positive interest rate gaps, the adoption of an inflation targeting regime and the current account balance seem to explain the duration of negative interest rate gaps. Our results suggest that the…

Model comparison and selection for stationary space–time models

2007

An intensive simulation study to compare the spatio-temporal prediction performances among various space-time models is presented. The models having separable spatio-temporal covariance functions and nonseparable ones, under various scenarios, are also considered. The computational performance among the various selected models are compared. The issue of how to select an appropriate space-time model by accounting for the tradeoff between goodness-of-fit and model complexity is addressed. Performances of the two commonly used model-selection criteria, Akaike information criterion and Bayesian information criterion are examined. Furthermore, a practical application based on the statistical ana…

Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter

2013

Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use adaptive MCMC algorithms which automatically tune the statistics of a proposal distribution during the MCMC run. A new adaptive MCMC algorithm, called the variational Bayesian adaptive Metropolis (VBAM) algorithm, is developed. The VBAM algorithm updates the proposal covariance matrix using the variational Bayesian adaptive Kalman filter (VB-AKF). A strong law of large numbers for the VBAM algorithm is…

Supercapacitor diagnosis using an Extended Kalman Filtering approach

2016

This paper deals with the model-based analysis of a Supercapacitor for diagnostic purposes. A two legs nonlinear physical model is assumed for the Supercapacitor and the corresponding second-order nonlinear state-space mathematical model is obtained. Then, an Extended Kalman Filter is tuned so that the estimated outputs reproduce the voltages at the equivalent capacitance terminals; they give information on the state of health of the supercapacitor but are not directly measurable. In particular, an optimization problem is firstly formulated, involving the experimental input-output data and those given by the Extended Kalman Filter.

Diagnostic of Supercapacitors based on State Estimation through Extended Kalman Filter

This thesis aimed to analyze the supercapacitor behavior to verify the possibility of the estimation of its internal parameters for diagnostic purposes. The target has been reached firstly by a theoretical analysis which showed that the crucial parameters for diagnostic purposes are the voltage of the non-linear capacitance, since it gives a measure of the stored charge, and the parasitic resistance which limits the maximum allowable current and influences losses. Secondly it has been verified when a supercapacitor parameters are observable and finally an extended Kalman filter to obtain such parameters has been set up. The Thesis demonstrated that a supercapacitor can be modelled by a 2-br…

A Kalman filter single point positioning for maritime applications using a smartphone

2020

Different positioning techniques have been largely adopted for maritime applications that require high accuracy kinematic positioning. The main objective of the paper is the performance assessment of a Single Point Positioning algorithm (SPP), with a Kalman filter (KF) estimator, adapted for maritime applications. The KF has been chosen as estimation technique due to the ability to consider both the state vector dynamic and the measurements. Particularly, in order to compute an accurate vertical component of the position, suitable for maritime applications, the KF settings have been modified by tuning the covariance matrix of the process noise. The algorithm is developed in Matlab environme…

Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter

2019

Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under ar…

Wind Shear On-Line Identification for Unmanned Aerial Systems

2014

An algorithm to perform the on line identification of the wind shear components suitable for the UAS characteristics has been implemented. The mathematical model of aircraft and wind shear in the augmented state space has been built without any restrictive assumption on the dynamic of wind shear. Due to the severe accelerations on the aircraft induced by the strong velocity variation typical of wind shear, the wind shear effects have been modeled as external forces and moments applied on the aircraft. The identification problem addressed in this work has been solved by using the Filter error method approach. An Extended Kalman Filter has been developed to propagate state. It has been tuned …

Tracking in Presence of Total Occlusion and Size Variation using Mean Shift and Kalman Filter

2011

International audience; The classical mean shift algorithm for tracking in perfectly arranged conditions constitutes a good object tracking method. However, in the real environment it presents some limitations, especially under the presence of noise, objects with varying size, or occlusions. In order to deal with these problems, this paper proposes a reliable object tracking algorithm using mean shift and the Kalman filter, which was added to the traditional algorithm as a predictor when no reliable model of the object being tracked is found. Experimental work demonstrates that the proposed mean shift Kalman filter algorithm improves the tracking performance of the classical algorithms in c…

Interference Estimation in IEEE 802.11 Networks

2010

This article describes a technique for distinguishing and quantifying medium access control (MAC) and physical layer (PHY) interference in error-prone 802.11 networks. This technique, is fully distributed, allowing each station to estimate interference individually. The estimator is based on an extended Kalman filter coupled to a mechanism for revealing abrupt changes in state. The network state is a vector of two components, representing PHY interference, expressed in terms of channel-error rate, and MAC interference. Two distinct state models are considered. When PHY interference can be assumed to be constant for all stations, network congestion is expressed by the number of competing ter…