Kalman Filter Simulation Based on Current Statistical Model

This is a Kalman filter simulation program based on the "current" statistical model. The Kalman filter is a widely used signal processing technique for estimating unknown state variables such as position, velocity, temperature, and other dynamic system parameters. It improves estimation accuracy by optimally combining sensor measurements with system model predictions through recursive mathematical operations. The implementation features a state-space representation where the system dynamics are modeled using state transition matrices and measurement models. The algorithm follows a two-step process: prediction step (using system model to project state forward) and update step (incorporating new measurements to refine estimates). Key functions include covariance matrix calculations, Kalman gain computation, and residual analysis for performance evaluation. This program demonstrates how the filter adapts to the current statistical characteristics of the system, providing users with practical insights into the technology's working principles. The code structure emphasizes modularity and extensibility, allowing users to modify system parameters, measurement models, and noise characteristics according to specific requirements. The simulation includes visualization components for tracking estimation errors and convergence behavior over time.