TY - GEN
T1 - Improved innovation-based adaptive estimation for measurement noise uncertainty in SINS/GNSS integration system
AU - Gao, Wei
AU - Li, Jingchun
AU - Zhang, Ya
AU - Wang, Guochen
AU - Sun, Xuran
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/18
Y1 - 2017/10/18
N2 - The Kalman filter (KF) is the most common method for the estimation problems of the integrated SINS/GNSS system, but its performance depends on the correct a priori knowledge of model dynamics and noise statistics. The GNSS measurement noise uncertainties will degrade the performance of the KF for the fixed measurement noise covariance matrix. To fulfill the accuracy requirements of the dynamic system, an improved innovation-based adaptive estimation (IAE) algorithm is proposed. Based on the IAE principle, a regulatory factor is introduced into the calculation of the gain matrix to solve the singular value problem during the matrix inverse operation, and cut down the estimation errors caused by measurement noise uncertainties. The performance of the proposed algorithm is evaluated by the Monte-Carlo simulations in the SINS/GNSS integration system and significant improvements on the filter performance have been achieved.
AB - The Kalman filter (KF) is the most common method for the estimation problems of the integrated SINS/GNSS system, but its performance depends on the correct a priori knowledge of model dynamics and noise statistics. The GNSS measurement noise uncertainties will degrade the performance of the KF for the fixed measurement noise covariance matrix. To fulfill the accuracy requirements of the dynamic system, an improved innovation-based adaptive estimation (IAE) algorithm is proposed. Based on the IAE principle, a regulatory factor is introduced into the calculation of the gain matrix to solve the singular value problem during the matrix inverse operation, and cut down the estimation errors caused by measurement noise uncertainties. The performance of the proposed algorithm is evaluated by the Monte-Carlo simulations in the SINS/GNSS integration system and significant improvements on the filter performance have been achieved.
KW - Innovation-based adaptive estimation
KW - Integration navigation
KW - Noise uncertainties
UR - https://www.scopus.com/pages/publications/85039922063
U2 - 10.1109/CPGPS.2017.8075091
DO - 10.1109/CPGPS.2017.8075091
M3 - 会议稿件
AN - SCOPUS:85039922063
T3 - 2017 Forum on Cooperative Positioning and Service, CPGPS 2017
SP - 22
EP - 28
BT - 2017 Forum on Cooperative Positioning and Service, CPGPS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Forum on Cooperative Positioning and Service, CPGPS 2017
Y2 - 19 May 2017 through 21 May 2017
ER -