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      KCI등재 SCIE SCOPUS

      Interacting Multiple Model Estimation-based Adaptive Robust Unscented Kalman Filter

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      https://www.riss.kr/link?id=A105038144

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      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.
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      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 di...

      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.

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      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

      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

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-12-29 학회명변경 한글명 : 제어ㆍ로봇ㆍ시스템학회 -> 제어·로봇·시스템학회 KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-10-29 학회명변경 한글명 : 제어ㆍ자동화ㆍ시스템공학회 -> 제어ㆍ로봇ㆍ시스템학회
      영문명 : The Institute Of Control, Automation, And Systems Engineers, Korea -> Institute of Control, Robotics and Systems
      KCI등재
      2005-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2004-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2002-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 1.35 0.6 1.07
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.88 0.73 0.388 0.04
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