Adaptive algorithm based on multi-Kalman filter for target tracking and simulation analyses

Because the state noise model is fixed and simplex in kalman filter, the result of tracking is unsatisfactory while the moving state of target changing acutely. In order to improve the tracking precision and convergence rate, a novel algorithm was proposed, multi-kalman filter with different sample time were used to judge the target moving state, and then adjust the state noise covariance adaptively. From the simulation, it can be seen that the algorithm proposed can improve the result of tracking.