Complete Algorithmic Framework for Maneuvering Target Tracking: Kalman, Extended Kalman, and Particle Filters with Simulation Code and Visualizations

In this work, we present a detailed implementation of a complete algorithmic framework for maneuvering target tracking, including Kalman Filter, Extended Kalman Filter, and Particle Filter algorithms with comprehensive simulation code and graphical results. Each algorithm features detailed implementation explanations covering key programming components such as state transition matrices, measurement update functions, and probabilistic sampling methods. The Kalman Filter implementation demonstrates linear system tracking with optimal state estimation techniques, while the Extended Kalman Filter handles nonlinear systems through Jacobian matrix calculations and local linearization approaches. The Particle Filter employs sequential Monte Carlo methods with importance sampling and resampling procedures to handle complex non-Gaussian distributions. We provide thorough analyses of each algorithm's strengths and limitations, along with optimal application scenarios based on computational complexity and tracking performance metrics. Through this comprehensive exploration, readers will gain deep insights into maneuvering target tracking methodologies and acquire practical skills for implementing these algorithms to solve real-world tracking challenges.