Browsing by Author "Ucuncu, Murat"

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    Improving The Performance of a MEMS-IMU System Based On A False State-Space Model By Using A Fading Factor Adaptive Kalman Filter
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-06-29) Akbas, Eren Mehmet; Cifdaloz, Oguzhan; Ucuncu, Murat
    In this study, we introduce a novel algorithm, the low error rate adaptive fading Kalman filter (LERAFKF), designed to predict system states in the presence of uncertainty in both the system matrix and the model. The purpose of developing the LERAFKF is to address challenges arising from measurement difficulties, system parameter uncertainties, and state-space model inaccuracies. Several studies have utilized the Kalman filter (KF) and extended Kalman filter (EKF) algorithms to handle uncertainties in system parameters, corrupted measurements with unknown covariances, and incorrectly defined system modeling. Our work distinguishes itself by proposing a new approach that achieves lower error and deviation rates by combining the current Kalman estimation algorithm and the fading factor adaptive filter. To achieve this goal, we transformed the KF into an adaptive KF by introducing a forgetting factor, and the algorithm was subsequently reconfigured to calculate an optimized forgetting factor. In this study, we conducted simulations and measurements using both linear and nonlinear systems. The linear system represents the motion of an object, and the simulation involved measurements from the inertial navigation system (INS) sensor, specifically the Pololu IMU01b three-axis inertial measurement unit (IMU) sensor. We employed the SDI33 system with 9 degrees of freedom (DoF) mounted on a three-axis rotary table for the nonlinear system. This system simulates a missile as a 4th-order nonlinear system. Our findings demonstrate that the proposed LERAFKF filter outperforms KF and EKF in estimating system states, particularly in measurement-related error scenarios. Mean square error analysis further confirmed that LERAFKF exhibited the lowest error values, showcasing superior performance over KF and EKF in linear and nonlinear systems.
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    RadGT: Graph and Transformer-Based Automotive Radar Point Cloud Segmentation
    (2023) Sevimli, Rasim A.; Ucuncu, Murat; Koc, Aykut; 0000-0002-2113-1398; KDO-6837-2024
    The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.
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    State of Charge (SOC) Estimation for Lithium-Ion Battery Cell Using Extended Kalman Filter
    (2019) Ucuncu, Murat; Altindag, Arda; GYI-8414-2022
    The State of Charge (SOC) estimation is an essential part of a Battery Management System. Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, therefore, it is hard to determine the state of charge of the battery cell. In this paper, a lithium iron phosphate battery cell with 8 Ah capacity and 3.2 Volt rated voltage was studied. The battery is modelled to reflect the dynamic of the battery encompassing mainly four elements; an Open Circuit Voltage (OCV) source, two RC network, and one resistor. The model parameters are identified by using Forgetting Factor Recursive Least Mean Squares and time domain extraction method. Parameters are converged to their real values and these values are used to estimate the state of charge of the cell using Extended Kalman Filter algorithm based on battery model dynamic. The results in our study show that SoC which is estimated by the implemented Extended Kalman Filter converges to battery's real SoC value. It is also shown that the Extended Kalman Filter update and prediction stages iterations move forward to minimize the error between real SoC and estimated SoC.