مرکز منطقه ای اطلاع رساني علوم و فناوري - Improvement for the Adaptive Kalman Filter with Multiple Fading Factors

Abstract :

TheKalman filter is most popular estimation technique for solving state estimation problems of dynamical systems and it has been the most frequently used algorithm in applications from different areas such as science, military and economics etc. The Kalman filter works best with predictive performance as long as system characteristics are known correctly. However, the performance of the Kalman filter will dramatically decrease when system characteristics are either unknown or partially known. Numerous studies have been published so far to get over the problem of performance loss in the Kalman filter. Some researchers introduced a fading factor to improve the performance of the Kalman filter under unknown or partially known initial information. “Adaptive estimation of multiple fading factors in Kalman filter for navigation applications” (AEMFFKF) is one of these studies. In this paper, adaptive fading Kalman filter with the multiple forgetting factors is considered and an adaptive estimation algorithm is proposed to determine forgetting factors which can not be determined in the AEMFFKF. In addition, A Monte Carlo simulation is performed to compare the estimation performances of the Kalman filter with the adaptive filters.