Nonlinear Kalman Filter Tracking System: Implementation and Error Analysis

This paper presents the implementation and error analysis of a nonlinear Kalman filter tracking system. Designed as a reference for beginners, the content helps readers understand fundamental principles of target tracking. We provide detailed explanations of the implementation process, covering system design and construction with specific references to algorithmic approaches. The implementation typically involves state prediction using nonlinear motion models and measurement update steps employing linearization techniques like Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF). Additionally, we conduct thorough error analysis discussions, examining covariance propagation and performance metrics to help readers evaluate system capabilities. Key functions include state transition modeling, Jacobian matrix calculations for EKF implementations, and sigma point transformations for UKF variants. The primary objective is to equip beginners with sufficient knowledge to comprehend nonlinear Kalman filter tracking principles and apply them in practical scenarios. We hope this article enhances understanding of target tracking systems and provides an improved learning experience through concrete implementation examples.