Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter

다국어 초록 (Multilingual Abstract)

The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamicsystems due to its simple calculation process and superior performance in highly nonlinear systems. However, itssolution will be degraded or even divergent when the system model involves uncertainty. This paper presents aninteracting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This methodcombines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbanceof system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process modeluncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principleof innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF androbust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilisticweighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparisonanalysis validate the efficacy of the proposed method.

참고문헌 (Reference)

1 S. S. Gao, "Windowing and random weighting-based adaptive unscented Kalman filter" 29 (29): 201-223, 2015

2 S. J. Julier, "Unscented filtering and nonlinear estimation" 92 (92): 401-422, 2004

3 김상봉, "Trajectory Tracking and Fault Detection Algorithm for Automatic Guided Vehicle Based on Multiple Positioning Modules" 제어·로봇·시스템학회 14 (14): 400-410, 2016

4 G. G. Hu, "Stochastic stability of the derivative unscented Kalman filter" 24 (24): 070202-, 2015

5 C. E. Seah, "State estimation for stochastic linear hybrid systems with continuous-state-dependent transitions : an IMM approach" 45 (45): 376-392, 2009

6 D. Y. Kim, "Square Root Receding Horizon Information Filters for Nonlinear Dynamic System Models" 58 (58): 1284-1289, 2013

7 S. Y. Cho, "Robust positioning technique in low-cost DR/GPS for land navigation" 55 (55): 1132-1142, 2006

8 Yan Zhao, "Robust Predictive Augmented Unscented Kalman Filter" 제어·로봇·시스템학회 12 (12): 996-1004, 2014

9 Shesheng Gao, "Random Weighting Estimation for Systematic Error of Observation Model in Dynamic Vehicle Navigation" 제어·로봇·시스템학회 14 (14): 514-523, 2016

10 W. Wang, "Quadratic extended Kalman ?lter approach for GPS/INS integration" 10 (10): 709-713, 2006

11 H. E. Soken, "Pico satellite attitude estimation via robust unscented Kalman filter in the presence of measurement faults" 49 (49): 249-256, 2010

12 K. Xiong, "Performance evaluation of UKF-based nonlinear filtering" 42 (42): 261-270, 2006

13 D. J. Jwo, "Performance enhancement for ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman filter" 73 (73): 377-395, 2013

14 N. J. Gordon, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation" 140 (140): 107-113, 1993

15 K. Xiong, "Modified unscented Kalman filtering and its application in autonomous satellite navigation" 13 (13): 238-246, 2009

16 G. G. Hu, "Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty" 29 (29): 1561-1577, 2015

17 G. B. Chang, "Kalman filter with both adaptivity and robustness" 24 (24): 81-87, 2014

18 L. B. Chang, "Huberbased novel robust unscented Kalman filter" 6 (6): 502-509, 2012

19 D. J. Jwo, "Fuzzy Adaptive Unscented Kalman Filter for Ultra-Tight GPS/INS Integration" 229-235, 2010

20 D. H. Zhou, "Extension of Friedland’s separate-bias estimation to randomly timevarying bias of nonlinear systems" 38 (38): 1270-1273, 1993

21 Y. Meng, "Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration" 120 : 171-181, 2016

22 L. A. Johnston, "An improvement to the interacting multiple model(IMM)algorithm" 49 (49): 2909-2923, 2001

23 D. J. Jwo, "An adaptive sensor fusion method with applications in integrated navigation" 61 (61): 705-721, 2008

24 Q. Song, "An adaptive UKF algorithm for the state and parameter estimation of a mobile robot" 34 (34): 72-79, 2008

25 Lei Wang, "Algorithm of Gaussian Sum Filter based on High-order UKF for Dynamic State Estimation" 제어·로봇·시스템학회 13 (13): 652-661, 2015

26 C. E. Seah, "Algorithm for performance analysis of the IMM algorithm" 47 (47): 1114-1124, 2011

27 H. E. Soken, "Adaptive fading UKF with Qadaptation : application to picosatellite attitude estimation" 26 (26): 628-636, 2011

28 Y. Shi, "Adaptive UKF method with applications to target tracking" 37 (37): 755-759, 2011

29 L. Zhao, "Adaptive UKF filtering algorithm based on maximum a posterior estimation and exponential weighting" 36 (36): 1007-1019, 2010

30 B. B. Gao, "Adaptive UKF based on maximum likelihood principle and receding horizon estimation" 38 (38): 1629-1637, 2016

31 S. Y. Cho, "Adaptive IIR/FIR fusion filter and its application to the INS/GPS integrated system" 44 (44): 2040-2047, 2008

32 김현식, "Adaptive Fuzzy IMM Algorithm for Uncertain Target Tracking" 제어·로봇·시스템학회 7 (7): 1001-1008, 2009

33 D. H. Zhou, "A suboptimal multiple fading extended Kalman filter" 17 (17): 689-695, 1991

34 G. G. Hu, "A derivative UKF for tightly coupled INS/GPS integrated navigation" 56 : 135-144, 2015