TWO NEW ALGORITHMS FOR ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN AND THEIR APPLICATIONS

From the point of view of time series analysis⁽¹⁾,based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering technique.New algorithms are simpler than that of Mehra⁽³⁾ and Tajima⁽⁴⁾.As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms.