Kalman Filter Simulation for Target Tracking

This program implements a Kalman filter simulation to accurately estimate target motion trajectories. The algorithm processes observation signals containing both distance and velocity measurements through a structured workflow: initial state estimation, time update (prediction phase using motion models), and measurement update (correction phase incorporating sensor data). The implementation features proper covariance management and gain optimization for stable filtering performance. The codebase has been rigorously validated for correctness and completeness, making it particularly suitable for colleagues with Kalman filter knowledge to study and exchange ideas. Additional functionalities include data visualization modules for plotting trajectory comparisons and result analysis tools for quantifying estimation errors. These enhancements help users better understand Kalman filter applications through practical implementation examples. Key algorithmic components include: - State transition matrix implementation for motion modeling - Measurement matrix configuration for sensor data fusion - Covariance propagation and Kalman gain calculation - Real-time trajectory smoothing and prediction We hope this program provides deep insights into Kalman filter principles and practical implementation, enabling users to effectively apply and optimize this method in real-world engineering scenarios.