Browsing by Subject "Kalman filter"

Now showing 1 - 9 of 9

Open Access
Adaptive tracking of narrowband HF channel response
(Wiley-Blackwell Publishing, 2003) Arikan, F.; Arıkan, Orhan
Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.
Open Access
Brent–Dubai oil spread: Basic drivers
(Economic Society of Australia Inc., 2021-12) Haliloğlu, E. H.; Şahin, S.; Berument, M. Hakan
This study aims to assess and quantify the effects of potential economic drivers on the Brent–Dubai price spread using the time-dependent Kalman filtering technique. To understand the pricing mechanism of crude oils, it is necessary to distinguish the driving forces of the world oil market. The Brent–Dubai price spread is selected as a global indicator representing trends in world oil trade and global economic activities. The estimates suggest that the global economic activities represented by the world trade index, world oil demand, world steel production, the number of world airline passengers and regional dynamics proxied by the growth rate of China over the US and the Euro Area have explanatory power on the Brent–Dubai spread.
Open Access
The effects of different inflation risk premiums on interest rate spreads
(Elsevier BV, 2004) Berument, Hakan; Kilinc, Z.; Ozlale, U.
This paper analyzes how the different types of inflation uncertainty affect a set of interest rate spreads for the UK. Three types of inflation uncertainty - structural uncertainty, impulse uncertainty, and steady-state inflation uncertainty - are defined and derived by using a time-varying parameter model with a GARCH specification. It is found that both the structural and steady-state inflation uncertainties increase interest rate spreads, while the empirical evidence for the impulse uncertainty is not conclusive. © 2003 Elsevier B.V. All rights reserved.
Open Access
Identification of hydrodynamic coefficients of AUV in the presence of measurement biases
(Sage Publications, 2021-11-23) Dinç, Mustafa; Hajiyev, C.
This paper mainly presents the parameter identification method developed from a Least Square Estimation (LSE) algorithm to estimate hydrodynamic coefficients of Autonomous Underwater Vehicle (AUV) in the presence of measurement biases. LSE based parameter determination method is developed to obtain unbiased estimated values of hydrodynamic coefficients of AUV from biased Inertial Navigation System (INS) measurements. The proposed parameter identification method consists of two phases: in the first phase, high precision INS and its auxiliary instrument including compass, pressure depth sensor, and Doppler Velocity Log (DVL) are designed as Integrated Navigational System coupled with Complementary Kalman Filter (CKF) to determine hydrodynamic coefficients of AUV by removing the INS measurement biases; in the second phase, LSE based parameter identification method is applied to the model in the first phase for obtaining unbiased estimated values of hydrodynamic coefficients of AUV. In this paper, a method for identifying the yaw and sway motion dynamic parameters of an AUV is given. Various maneuvering scenarios are verified to assess the parameter identification method employed. The simulation results indicate that using the CKF based Integrated Navigation System together with unbiased measurement conversion could produce better results for estimating the hydrodynamic coefficients of AUV.
Open Access
Input sequence estimation and blind channel identification in HF communication
(1999) Ben Hadj Miled, Mohamed Khames
Recent advances in blind channel equalization approaches and the availability of fast processors have made it possible to communicate reliably over long distances through HF communication links. Current research efforts focus on the improvement of the performance of the communication systems which degrades significantly during "bad tropospheric conditions" when the channel characteristics show rapid variations. In order to improve the performance of the HF communication links during these conditions, algorithms that can identify and track the channel characteristics are proposed in this thesis. Detailed simulation based comparisons with the existing algorithms show that the proposed approaches significantly improve the performance of the communication system and enable us to utilize HF communication in bad conditions even at 10 dB SNR.
Open Access
Is there a flight to quality due to inflation uncertainty?
(Elsevier BV, 2005) Guler, B.; Ozlale, U.
After two types of inflation uncertainty are derived within a time-varying parameter model with GARCH specification, the relationship between inflation uncertainty and interest rates for safe assets is investigated. The results support the existence of a "flight to quality" effect. © 2004 Elsevier B.V. All rights reserved.
Open Access
A measure of the informal sector for the Turkish diesel market
(Taylor & Francis, 2021-07-09) Yaz, H. F.; Dogan, N.; Berument, M. Hakan
Measuring the size of the informal diesel market is important for determining tax revenue losses and identifying inefficiencies in tax policies. The conventional ways of assessing the informal sector entail either not allowing the measurement of the size of the informal sector across time or assuming a stable relationship between diesel consumption and a set of economic variables. This study assesses the informal sector of the Turkish diesel market by using the Kalman filter method. This method allows unobserved values to be estimated with observed variables. Using monthly interpolated GDP, official diesel consumption, and the number of diesel motor vehicles, Turkey’s unobserved informal diesel fuel consumption between January 2005 and February 2020 is estimated. The results obtained with this estimation method reveal that the level of informal diesel consumption increased until 2012–2014; it then started to decline at the end of 2014 and started to increase again after 2018. These dates are associated with periods of economic recession, political developments, and the passing of anti-smuggling legislation.
Open Access
Robust minimax estimation applied to kalman filtering
(2008) Aybar, Bahadır
Kalman filtering is one of the most essential tools in estimating an unknown state of a dynamic system from measured data, where the measurements and the previous states have a known relation with the present state. It has generally two steps, prediction and update. This filtering method yields the minimum mean-square error when the noise in the system is Gaussian and the best linear estimate when the noise is arbitrary. But, Kalman filtering performance degrades significantly with the model uncertainty in the state dynamics or observations. In this thesis, we consider the problem of estimating an unknown vector x in a statespace model that may be subject to uncertainties. We assume that the model uncertainty has a known bound and we seek a robust linear estimator for x that minimizes the worst case mean-square error across all possible values of x and all possible values of the model matrix. Robust minimax estimation technique is derived and analyzed in this thesis, then applied to the state-space model and simulation results with different noise perturbation models are presented. Also, a radar tracking application assuming a linear state dynamics is also investigated. Modifications to the James-Stein estimator are made according to the scheme we develop in this thesis, so that some of its limitations are dealt with. In our scheme, James-Stein estimation can be applied even if the observation equation is perturbed and the number of observations are less than the number of states, still yielding robust estimations.
Open Access
Time-varying cointegration and the Kalman filter
(Taylor and Francis, 2022) Eroğlu, B. A.; Miller, J. I.; Yiğit, Taner
We show that time-varying parameter state-space models estimated using the Kalman filter are particularly vulnerable to the problem of spurious regression, because the integrated error is transferred to the estimated state equation. We offer a simple yet effective methodology to reliably recover the instability in cointegrating vectors. In the process, the proposed methodology successfully distinguishes between the cases of no cointegration, fixed cointegration, and time-varying cointegration. We apply these proposed tests to elucidate the relationship between concentrations of greenhouse gases and global temperatures, an important relationship to both climate scientists and economists.