Kalman Filter Tracking Implementation

This article discusses the implementation of Kalman filter tracking programs. Kalman filtering is widely used for system state estimation, particularly in signal processing and control engineering applications. The algorithm is based on Bayesian principles, utilizing measurement data and state transition equations to estimate system states. In practical implementations, least squares tracking represents a variant of Kalman filtering that optimizes state estimation by minimizing the sum of squared measurement errors. The IMM tracking approach employs a multiple model methodology that estimates different physical system models and selects the optimal model based on the signal-to-noise ratio of observed values. Our Kalman filter tracking implementation successfully incorporates both methods to enhance tracking accuracy and reliability. The code structure typically includes key functions for state prediction, measurement update, covariance matrix handling, and model probability calculations for IMM algorithm. The implementation uses recursive filtering techniques where each iteration processes new measurements while maintaining historical state information through covariance propagation.