Multi-Target Tracking (3D) - MATLAB Algorithm Implementation

In this article, we provide a comprehensive overview of 3D multi-target tracking algorithms and present step-by-step implementation using MATLAB. We begin by exploring fundamental concepts and principles of multi-target tracking, along with essential knowledge required for implementing these algorithms in three-dimensional space. The discussion covers key algorithmic components such as Kalman filters for state estimation, data association techniques (like Global Nearest Neighbor or JPDA), and trajectory management in 3D coordinate systems. Subsequently, we introduce core MATLAB programming concepts including syntax structures and essential built-in functions relevant to tracking implementations, such as matrix operations for coordinate transformations and visualization tools for 3D trajectory plotting. Finally, we provide detailed implementation steps and practical code examples demonstrating how to initialize tracking parameters, process sensor data, and visualize results using MATLAB's plotting capabilities. Through this guide, readers will gain deep understanding of 3D multi-target tracking algorithms and acquire practical MATLAB programming skills for implementing these algorithms in real-world applications.