Simulation Studies on Passive Localization
Simulation code for passive localization, including 2D linear, nonlinear, maximum likelihood TOA algorithms with Cramer-Rao lower bound and mean square error implementations
Nonlinear Least Squares Fitting: Optimized Implementation and Algorithm Insights
Successfully implemented nonlinear least squares fitting with intensive debugging and significant code optimization. This implementation utilizes advanced algorithms and demonstrates improved computational efficiency.
Nonlinear Simulink Simulation Model for F-16 Aircraft
A MATLAB-implemented nonlinear Simulink simulation model designed for F-16 aircraft demonstration, featuring aerodynamic modeling and flight dynamics analysis with customizable simulation parameters.
SVM Currently Primarily Applied in Fault Diagnosis
Support Vector Machine (SVM) is widely used in fault diagnosis due to its effectiveness in addressing nonlinear problems and handling small sample datasets.
Simulink Modeling and Simulation of Lead-Acid Batteries Based on Third-Order Model
Addressing the nonlinear, complex, and environmentally sensitive characteristics of lead-acid battery models, we implemented a third-order model for lead-acid battery modeling. The simulation results provide corresponding conclusions. We propose novel approaches utilizing "black-box" theory from artificial intelligence, neural network theory, and adaptive control concepts to resolve challenges in lead-acid battery simulation modeling. Key implementation aspects include MATLAB's Simscape Electrical components for battery parameterization and Stateflow for adaptive algorithm integration.
Particle Filter for Target Tracking
Particle Filter Target Tracking in One-Dimensional Case for Nonlinear Non-Gaussian Systems with Implementation Details
Extended Kalman Filter: The Standard Nonlinear Kalman Filter
The Extended Kalman Filter serves as the standard nonlinear Kalman filter implementation; this comprehensive toolbox includes various commonly used Extended Kalman Filter variants with practical code examples and algorithm explanations.
Kernel Principal Component Analysis Method
Kernel Principal Component Analysis is an enhanced algorithm based on Principal Component Analysis, serving as a nonlinear feature extraction technique that utilizes kernel functions to map data into higher-dimensional spaces for improved pattern recognition.
Superior Performance of Particle Filter Algorithm for Nonlinear Non-Gaussian Signal Processing
Leveraging the superior performance of particle filter algorithm for nonlinear non-Gaussian signal processing, this method is applied to modal signal and vibration signal denoising with implementation details including sequential Monte Carlo sampling and importance weighting techniques.
Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD)
Empirical Mode Decomposition (EMD) is an adaptive signal decomposition method primarily applied to nonlinear and non-stationary signals. Ensemble Empirical Mode Decomposition (EEMD) addresses the mode mixing problem inherent in standard EMD. Implementation typically involves iterative sifting processes using MATLAB's signal processing toolbox or Python libraries like PyEMD.