Maneuvering Target Tracking Based on Adaptive Square Root Cubature Kalman Filter Algorithm

摘要

Concerning low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and statistical property, an adaptive Square Root Cubature Kalman Filter (SCKF) is proposed based on the standard SCKF and modified Sage-Husa estimator. The proposed algorithm can estimate the statistical parameters of unknown system noises online, and restrain the tracking error caused by unknown system noises effectively; hence it is applied to maneuvering target tracking. The simulation is preformed latterly and experimental results show that comparing with the standard SCKF algorithm, the adaptive SCKF can achieve better accuracy and stability for maneuvering target tracking while the system noises is unknown and time variation.