Alpha-Beta Filter: A Constant-Gain Filter for Uniform Motion Target Models

Based on user requirements, I have expanded the text while preserving its core concepts. The alpha-beta filter is a constant-gain tracking filter specifically designed for targets moving with uniform motion. This implementation includes simulation code that models three distinct operational scenarios: when target maneuver indicators are known in advance, when they are completely unknown, and when using adaptive filtering techniques. The simulation demonstrates key filter functions including state prediction (position and velocity updates), measurement integration, and gain adaptation mechanisms. These simulations help researchers and engineers better understand the filter's performance characteristics, including tracking accuracy, convergence behavior, and responsiveness to maneuver changes. The code structure typically includes initialization routines for filter parameters, prediction-update cycles implementing the alpha-beta equations, and performance evaluation modules calculating metrics like RMS error. Adaptive implementations may feature maneuver detection algorithms and dynamic gain adjustment logic. I hope this enhanced version meets your requirements for comprehensive technical documentation.