MATLAB-Based Single Target Tracking Algorithm Implementation

This paper presents a single target tracking algorithm program developed in MATLAB. The program employs recursive algorithms and incorporates advanced techniques including Maximum Likelihood Estimation, Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter. Each algorithm is meticulously implemented with detailed inline comments to facilitate understanding of both functionality and implementation methodology. The implementation features proper state initialization, recursive prediction-correction cycles, and covariance matrix handling for optimal tracking performance. We provide comprehensive explanations of each algorithm's underlying principles, advantages, and limitations, along with practical application scenarios. The code structure includes modular functions for state prediction, measurement update, and likelihood calculation, ensuring maintainability and scalability. Furthermore, we discuss potential enhancements for improving program stability and reliability, such as adaptive noise covariance tuning, robustness to occlusion handling, and computational efficiency optimization through matrix operation vectorization. This work aims to support researchers and engineers in related fields by providing a well-documented reference implementation for single target tracking systems.