Kalman and Wiener Filtering: Implementation and Algorithms

During my graduate studies, I undertook a comprehensive modern signal processing course that covered advanced topics including Kalman and Wiener filtering. Through hands-on MATLAB programming exercises, I implemented these filters using key signal processing functions: for Kalman filtering, I utilized state-space modeling with kalman function for optimal state estimation, while for Wiener filtering, I employed wiener2 for image processing applications and designed FIR filters using Wiener-Hopf equations for signal denoising. The implementation involved creating system models with process and measurement noise covariance matrices (Q and R), and applying recursive prediction-correction algorithms. This coursework enabled me to master signal analysis techniques, design digital signal processing systems, and solve practical engineering problems. The experience provided deep insights into optimal filtering algorithms and established a strong foundation for my subsequent research in digital signal processing.