Title :
A novel adaptive unscented Kalman filter
Author :
Hu, Gaoge ; Gao, Shesheng ; Xue, Li
Author_Institution :
Sch. of Automatics, Northwestern Polytech. Univ., Xian, China
Abstract :
For solving the problem that the conventional unscented Kalman filter (UKF) declines in accuracy and further diverges when the system´s noise statistics are unknown and time-varying, an adaptive UKF is proposed based on moving window and random weighting methods. The moving window estimation defined in linear system is generalized to the nonlinear filter - UKF. The noise statistics are calculated by applying the moving window estimation and then the weights on each window are adjusted by utilizing the random weighting method. The proposed algorithm has the ability to estimate and adjust the noise statistics online, making the best of the moving window and the random weighting methods. Simulation and comparison analysis demonstrate that the proposed adaptive UKF performs much better than the standard UKF under the condition that system´s noise statistics are unknown and time-varying.
Keywords :
adaptive Kalman filters; estimation theory; linear systems; nonlinear filters; random processes; statistical analysis; adaptive UKF; adaptive unscented Kalman filter; linear system; moving window estimation; moving window method; noise statistics; nonlinear filter; random weighting method; Estimation error; Finite impulse response filter; Kalman filters; Noise; Vectors;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-2144-1
DOI :
10.1109/ICICIP.2012.6391482