Adaptive tracking window adjustment algorithm based on α-β-γ tracking filter with parameter identification

Traditional α-β-γ filter is still deficient in tracking targets of high maneuverability. An improved adaptive α-β-γ filter based on parameters identification is proposed which can apply fuzzy sets to make both tracking accuracy and convergence rate acceptable for such targets. The output of fuzzy logic system with the tracking error vector of the proposed α-β-γ filter is adjusted according to the variety of locomotive target. Then we can get the adaptive coefficients can be obtained, and this could make the tracking results converge quickly without decreasing the tracking accuracy. It can be used in the improved adaptive tracking window adjustment algorithm. The windows can be modified with different target environments. The simulation results show that the proposed tracking algorithm does ensure that both tracking accuracy and convergence rate can be acceptable for targets of high maneuverability in real-time.