Simulation of System Parameter Identification Based on Kalman Filter

System parameter identification problem has been the focus of world problems.The traditional Kalman filter algorithm can not improve the tracking accuracy and computational complexity.In order to solve the problem of practical system identification parameters of the noise variance and observation noise variance unknown and other related issues,based on the advantages of the standard Kalman filter,an algorithm was proposed An improved system was based on the unscented Kalman filter parameter identification method.The simulation results show that the algorithm has better generalization ability,and in the circumstances of complex system loads,the parameters of the system can also be effectively identified,which shows that the algorithm is an effective method for identification of system parameters.