Kalman Filter Tracking and Filtering Implementation

In this project, we will implement Kalman filter tracking and filtering functionality using MATLAB. The Kalman filter is a state estimation algorithm suitable for linear systems, designed to estimate system states while minimizing noise interference. Our implementation will begin with theoretical foundations, covering basic Kalman filter concepts and mathematical formulations. We will then transition to practical implementation within the MATLAB environment. The project structure follows a progressive learning approach, starting with simple examples and advancing to complex applications. Initial implementations will focus on basic state-space modeling using MATLAB's matrix operations, followed by covariance matrix initialization and recursive prediction-update cycles. We will demonstrate key functions including: - State prediction using system dynamics matrices - Measurement update integration with observation models - Covariance propagation and Kalman gain calculation As we advance, we'll explore different application scenarios such as object tracking, sensor fusion, and noise reduction in dynamic systems. The code implementation will emphasize: - Proper handling of process noise (Q) and measurement noise (R) matrices - Efficient matrix operations for real-time performance - Visualization of filtering results using MATLAB's plotting capabilities Through this project, you will gain solid knowledge in MATLAB programming and practical application of Kalman filters, establishing a strong foundation for future learning and professional work in signal processing and control systems.