MATLAB Implementation for Multi-Target Processing

This article presents a MATLAB-based multi-target processing program incorporating various algorithms such as IMM (Interacting Multiple Model) and JPDA (Joint Probabilistic Data Association). These implementations serve as excellent learning tools for students exploring information fusion techniques. The IMM algorithm represents a sophisticated multi-model filter that handles multiple system models simultaneously, significantly improving estimation accuracy through model interaction and probability calculations. In our MATLAB implementation, this involves maintaining multiple parallel filters with Markovian transitions between models. JPDA algorithm provides a probabilistic data association framework for tracking and identifying multiple targets, effectively handling measurement-to-track correspondence uncertainties. Our code implementation demonstrates how to calculate association probabilities and manage complex multi-target scenarios. Additionally, the program includes other fundamental filtering algorithms such as the Kalman Filter (for linear systems) and Extended Kalman Filter (for nonlinear systems). Each algorithm is implemented with clear structure and commented code, allowing students to examine the mathematical formulations directly in MATLAB syntax. The integrated application of these algorithms offers deep insights into information fusion principles and practical implementations. Through hands-on experimentation with this codebase, students can develop stronger foundations for academic research and professional careers in target tracking, autonomous systems, and sensor fusion technologies. The modular design enables easy extension and customization for specific research needs.