MATLAB Simulation of Uniformly Accelerated Target Motion with Kalman and Alpha-Beta-Gamma Filtering

This article explores MATLAB simulation techniques for uniformly accelerated target motion. We will implement two distinct filtering approaches - Kalman filtering and alpha-beta-gamma (α-β-γ) filtering, with specific applications to both Singer model and "current" statistical model implementations. The article provides detailed explanations of the underlying principles and implementation processes for these filtering methods, including code demonstrations that showcase their performance through simulation examples. Key MATLAB functions such as kalman for state estimation and custom implementations for α-β-γ filters will be discussed, along with motion model equations for acceleration handling. We will also analyze the comparative advantages and limitations of each filtering approach, providing guidance on selecting appropriate methods based on specific application scenarios. Through this comprehensive study, readers will gain deep understanding of uniformly accelerated motion simulation and practical skills for MATLAB implementation, including parameter tuning considerations and performance evaluation metrics for tracking algorithms.