Browsing by Subject "Kalman filtering"

Now showing 1 - 12 of 12

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
Dynamic obstacle avoidance with a prototype mobile robot using acoustic, infrared and position sensing
(1997) Kurbay, Saner
In this study, a small mobile robot is designed and built which employs infrared and acoustic sensors for detecting obstacles in the environment and a computer mouse for position sensing* that is installed underneath the robot. The robot is suitably designed for many robotics and sensing applications. The design of the robot and the dynamic obstacle avoidance algorithm are discussed in this study. The mobile robot is used in a real time dynamic obstacle avoidance application successfully. Linear Kalman hlter is employed in the smoothing of the obstacle's measured coordinates and velocities. Full autonomous operation is possible by updating the EPROM of the robot such that the dynamic obstacle avoidance algorithm is on the robot itself, not on the computer.
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
An efficient and effective second-order training algorithm for LSTM-based adaptive learning
(IEEE, 2021-04-07) Vural, N. Mert; Ergüt, S.; Kozat, Süleyman S.
We study adaptive (or online) nonlinear regression with Long-Short-Term-Memory (LSTM) based networks, i.e., LSTM-based adaptive learning. In this context, we introduce an efficient Extended Kalman filter (EKF) based second-order training algorithm. Our algorithm is truly online, i.e., it does not assume any underlying data generating process and future information, except that the target sequence is bounded. Through an extensive set of experiments, we demonstrate significant performance gains achieved by our algorithm with respect to the state-of-the-art methods. Here, we mainly show that our algorithm consistently provides 10 to 45% improvement in the accuracy compared to the widely-used adaptive methods Adam, RMSprop, and DEKF, and comparable performance to EKF with a 10 to 15 times reduction in the run-time.
Open Access
Efficient online learning algorithms based on LSTM neural networks
(Institute of Electrical and Electronics Engineers, 2018) Ergen, Tolga; Kozat, Süleyman Serdar
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.
Open Access
Efficient online training algorithms for recurrent neural networks
(2020-12) Vural, Nuri Mert
Recurrent Neural Networks (RNNs) are widely used for online regression due to their ability to learn nonlinear temporal dependencies. As an RNN model, Long-Short-Term-Memory Networks (LSTMs) are commonly preferred in prac-tice, since these networks are capable of learning long-term dependencies while avoiding the exploding gradient problem. On the other hand, the performance improvement of LSTMs usually comes with the price of their large parameter size, which makes their training signiﬁcantly demanding in terms of computational and data requirements. In this thesis, we address the computational challenges of LSTM training. We introduce two training algorithms, designed for obtaining the online regression performance of LSTMs with less computational requirements than the state-of-the-art. The introduced algorithms are truly online, i.e., they do not assume any underlying data generating process and future information, except that the dataset is bounded. We discuss theoretical guarantees of the introduced algo-rithms, along with their asymptotic convergence behavior. Finally, we demon-strate their performance through extensive numerical studies on real and synthetic datasets, and show that they achieve the regression performance of LSTMs with signiﬁcantly shorter training times.
Open Access
Estimation of object location and radius of curvature using ultrasonic sonar
(Elsevier, 2001-07) Sekmen, A. Ş.; Barshan, B.
Acoustic sensors are very popular in time-of-flight (TOF) ranging systems since they are inexpensive and convenient to use. One of the major limitations of these sensors is their low angular resolution which makes object localization difficult. In this paper, an adaptive multisensor configuration consisting of three transmitter/receiver ultrasonic transducers is introduced to compensate for the low angular resolution of sonar sensors and improve the localization accuracy. With this configuration, the radius of curvature and location of cylindrical objects are estimated. Two methods of TOF estimation are considered: thresholding and curve-fitting. The bias-variance combinations of these estimators are compared. Theory and simulations are verified by experimental data from a real sonar system. Extended Kalman filtering is used to smooth the data. It is shown that curve-fitting method, compared to thresholding method, provides about 30% improvement in the absence of noise and 50% improvement in the presence of noise. Moreover. the adaptive configuration improves the estimation accuracy by 35-40%. (C) 2001 Elsevier Science Ltd. All rights reserved.
Open Access
Implementation of a state-space Kalman filter on a digital signal processing microprocessor
(1990) Islam, M. Khaledul
Open Access
Input sequence estimation and blind channel identification in HF communication
(IEEE, 2000) Khames, Mariam; Miled, B. H.; Arıkan, Orhan
A new algorithm is proposed for reliable communication over HF tropospheric links in the presence of rapid channel variations. In the proposed approach, using fractionally space channel outputs, sequential estimation of channel characteristics and input sequence is performed by utilizing subspace tracking and Kalman filtering. 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.
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
Location and curvature estimation of "spherical" targets using a flexible sonar configuration
(IEEE, 1996) Barshan, Billur
A novel, flexible, three-dimensional (3-D) multi-sensor sonar system is employed to localize the center of a spherical target and estimate its radius of curvature. The interesting limiting cases for the problem under study are the point and planar targets, both of which are important for the characterization of a mobile robot's environment. A noise model is developed based on real sonar data. An extended Kalman filter (EKF) which incorporates the developed noise model is employed as an estimation tool for optimal processing of the sensor data. Simulations and experimental results are provided for specularly reflecting cylindrical targets.
Open Access
Neural networks based online learning
(IEEE, 2017) Ergen, Tolga; Kozat, Süleyman Serdar
In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the optimal parameter estimation under certain assumptions. We achieve this performance with a computational complexity in the order of the first order gradient based methods by controlling the number of particles. The experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods.