Implementation of Maneuvering Target Tracking Using Kalman Filter

This article presents a program implementation for maneuvering target tracking using a Kalman filter, based on the current statistical model. The implementation includes several key components: state vector initialization with position and velocity parameters, process noise modeling for maneuver acceleration, and measurement update integration with sensor data. The core algorithm handles both prediction steps (using state transition matrices) and correction steps (incorporating measurement innovations through Kalman gain calculation). The statistical model effectively captures target maneuver characteristics through adaptive noise covariance adjustments. We provide detailed explanations of Kalman filter implementation specifics, the concept of maneuvering target tracking, and the role of statistical models in enhancing tracking performance. The code structure includes modular functions for prediction, update, and performance evaluation, making it suitable for both educational purposes and practical applications. This resource aims to help you better understand and apply this tracking methodology in various scenarios.