http://scholars.ntou.edu.tw/handle/123456789/216 Mon, 10 Aug 2026 19:44:42 GMT 2026-08-10T19:44:42Z https://scholars.ntou.edu.tw:443/retrieve/98/通訊與導航工程學系.png http://scholars.ntou.edu.tw/handle/123456789/216 http://scholars.ntou.edu.tw/handle/123456789/26805 標題: Robust Nonlinear GNSS Navigation Under Heavy-Tailed Measurement Noise Using a Cauchy-Kernel Correntropy Extended Kalman Filter 作者: Jwo, Dah-Jing; Abdi, Abdirisak Daud; Chang, Yi 摘要: In urban canyon environments, the performance of global navigation satellite systems (GNSS) is severely degraded by multipath propagation, signal occlusion, and non-Gaussian measurement noise. These effects reduce positioning accuracy and service reliability. Kalman filtering and its nonlinear extensions are widely used for GNSS positioning. However, these filters rely on Gaussian noise assumptions and are formulated using the minimum mean square error (MMSE) criterion. Consequently, their performance degrades in non-Gaussian environments. Recently, filtering methods based on the maximum correntropy criterion (MCC) have been investigated as robust alternatives to MMSE-based approaches. The performance of MCC filters is strongly influenced by the choice of the kernel function. In particular, Gaussian kernel-based MCC algorithms may suffer from numerical instability under large measurement noise and strong sensitivity to kernel bandwidth selection. These limitations compromise estimation robustness and convergence stability. To address these limitations, this study proposes a Cauchy kernel-based maximum correntropy extended Kalman filter (CKMCEKF). The effectiveness of the proposed filter was validated using both simulation and real GNSS datasets. The results confirm the superior accuracy, stability, and reduced sensitivity to kernel bandwidth selection of the proposed method. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26805 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26632 標題: UAV Real-Time Image Recognition Using Lightweight YOLOv11 作者: Zhang, Xin-Yu; Juang, Jih-Gau 摘要: Unmanned aerial vehicles (UAVs) for environmental monitoring typically rely on embedded platforms with limited computational capacity, which constrains the deployment of highly accurate yet computationally demanding object-detection models. To address this challenge and enable real-time image recognition under resource limitations, this study develops three lightweight neural network architectures based on the YOLOv11 framework. The proposed designs aim to significantly reduce computational complexity and parameter count while maintaining stable and reliable detection performance, thereby improving inference efficiency and deployment flexibility on UAV platforms. YOLOv11-M is selected as the baseline model due to its favorable trade-off between detection accuracy and inference speed. Three lightweight strategies are then proposed and evaluated. First, a Ghost Convolution approach replaces portions of standard convolution with low-cost linear operations, effectively reducing both parameter size and computational overhead during feature extraction. Second, MobileNetV4 is employed as the backbone network; its optimized bottleneck structures and attention mechanisms enable substantial model compression without compromising recognition performance. Third, a MobileOne architecture with reparameterization is introduced, in which multi-branch structures enhance feature learning during training and are subsequently merged into a single-path network for inference, thereby significantly reducing computational cost and improving practical deployability. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26632 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26576 標題: Enhanced GNSS Navigation Using a Centered Error Entropy Extended Kalman Filter in Non-Gaussian Noise Environments 作者: Chang, Yi; Jwo, Dah-Jing; Lee, Bo-Yang 摘要: Global Navigation Satellite Systems (GNSSs) observables, such as those of the Global Positioning System (GPS), are frequently affected by multipath effects that cause unpredictable signal interference at the receiver, posing significant challenges for accurate state estimation in complex environments with non-Gaussian noise or outliers. The traditional extended Kalman filter (EKF), based on the minimum mean square error (MMSE) criterion, assumes Gaussian noise distributions and exhibits degraded performance under non-Gaussian conditions. To overcome this limitation, the minimum error entropy (MEE) criterion was proposed to reduce random uncertainty in estimation error distributions; however, due to its translation invariance property, MEE may inadvertently increase bias when errors contain systematic offsets, leading to poor convergence. In contrast, the maximum correntropy criterion (MCC) concentrates the error probability density function (PDF) around zero, enabling effective entropy adjustment even in the presence of bias and achieving superior error convergence. This paper presents the centered error entropy (CEE) extended Kalman filter (CEE-EKF) that integrates the complementary merits of both MEE and MCC approaches to overcome their individual limitations. Experimental validation in complex nonlinear GPS environments with non-Gaussian noise demonstrates that the CEE-EKF significantly outperforms individual algorithms in noise suppression, particularly exhibiting enhanced robustness and accuracy when handling outliers. These results offer an effective approach to enhancing the reliability of GPS navigation in challenging real-world environments, and the algorithm can be readily extended to other GNSS applications. Thu, 01 Jan 2026 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26576 2026-01-01T00:00:00Z http://scholars.ntou.edu.tw/handle/123456789/26508 標題: Nonlinear System Control Based on an Output Recurrent TS Fuzzy Controller 作者: Chiu, Chih-Hui 摘要: The Takagi-Sugeno (TS) fuzzy control design method has been widely utilized for nonlinear system control due to its ability to approximate complex system behaviors. Traditionally, TS fuzzy controllers are designed by computing controller gains offline using the MATLAB linear matrix inequality (LMI) control toolbox, where system parameters are obtained through the linearization of each subsystem. While this simplification reduces computational complexity, it also compromises the nonlinear characteristics of the system, leading to a discrepancy between the mathematical model and the real-world implementation. Moreover, system uncertainties and time-varying dynamics introduce further challenges, as the offline-derived controller gains may become suboptimal during actual operation, limiting the controller's adaptability and robustness. To address these issues, this study proposes an output recurrent Takagi-Sugeno fuzzy controller (ORTSFC), designed to enhance system adaptability and disturbance rejection. The novelty of this research lies in the integration of a recurrent structure within the TS fuzzy framework, transforming it from a static system into a dynamic one. This approach allows the controller to adapt continuously to real-time system dynamics, enhancing responsiveness and performance without requiring complex recalculations. Consequently, the proposed ORTSFC achieves robust control with significantly reduced computational demands. In addition, system stability is rigorously ensured through Lyapunov-based analysis. Overall, the ORTSFC provides a theoretically grounded and practically efficient solution for controlling nonlinear systems, especially under uncertainty and time-varying conditions. The main contributions of this study include: 1) Development of an ORTSFC, which introduces recurrence into the TS fuzzy control framework, improving its ability to capture dynamic system behaviors and adapt to uncertainties. 2) Application of the ORTSFC to a nonlinear control problem, specifically for the omnidirectional inverted pendulum (OIP), demonstrating its superior performance in stabilizing complex systems under uncertain conditions. Extensive simulation results validate the effectiveness of the proposed ORTSFC, showing its advantages over conventional TS fuzzy controllers in terms of adaptability, robustness, and computational efficiency. Wed, 01 Jan 2025 00:00:00 GMT http://scholars.ntou.edu.tw/handle/123456789/26508 2025-01-01T00:00:00Z