Parameter Optimization for a Third-Order Sampled-Data Tracker

This paper presents a searching method for parameters optimization of a third-order sampled-data tracker, which is called alpha-beta-gamma filter. The filter not only can track the position, velocity and acceleration signal, but also can reduce the measurement noise. In order to design an optimal third-order tracker, we propose utilizing a real-coded genetic algorithm (GA) to search the suitable parameter values for the alpha-beta-gamma filter. The experimental results indicate the optimized alpha-beta-gamma-filter based on GA can provide the approximate position, velocity and acceleration signal and simultaneously decrease the noise disturbance as much as possible.