A novel ELM based adaptive Kalman filter tracking algorithm

In order to avoid the filter divergence problem in target tracking caused by the unknown or changing statistical characteristic of the noise in Kalman filter, a novel ELM based adaptive Kalman filter tracking algorithm is proposed in this paper. By learning the difference between the theoretical covariance and practical covariance of the innovation which is defined as measurement residue through ELM, the adaptive factor of the covariance matrix of the observation noise was obtained. Then the covariance matrix of the observation noise can be adjusted online according to the ELM learning data. Simulation results showed that the proposed algorithm can improve the estimation accuracy and the robustness of the Kalman filtering for target tracking. It is also applied in the gaze tracking system for pupil tracking and shows satisfactory results.