TY - GEN
T1 - An Adaptive Robust Filter for GNSS/INS Integrated Navigation System
AU - Zhao, Chunhui
AU - Chen, Anqi
AU - Hua, Lin
AU - Lyu, Yang
AU - Li, Yanbo
N1 - Publisher Copyright:
© Beijing HIWING Scientific and Technological Information Institute 2024.
PY - 2024
Y1 - 2024
N2 - The integration of the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) capitalizes on their complementary attributes to provide dependable position data. This study introduces an innovative technique that utilizes an interactive multiple model-based adaptive robust enhanced Kalman filter (IMM-AREKF) algorithm. The algorithm is designed to tackle challenges like skewed GNSS observations due to a biased system model or substantial instantaneous measurement discrepancies. It enhances the accuracy of GNSS/INS navigation systems by dealing with issues such as system model ambiguity, errors in noise statistics, short-lived interference during the measurement phase, among others. The efficacy of the algorithm is confirmed through a simulation experiment, the outcome of which attests to its utility.
AB - The integration of the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) capitalizes on their complementary attributes to provide dependable position data. This study introduces an innovative technique that utilizes an interactive multiple model-based adaptive robust enhanced Kalman filter (IMM-AREKF) algorithm. The algorithm is designed to tackle challenges like skewed GNSS observations due to a biased system model or substantial instantaneous measurement discrepancies. It enhances the accuracy of GNSS/INS navigation systems by dealing with issues such as system model ambiguity, errors in noise statistics, short-lived interference during the measurement phase, among others. The efficacy of the algorithm is confirmed through a simulation experiment, the outcome of which attests to its utility.
KW - Extended Kalman filter
KW - Integrated navigation
KW - Interactive multiple model (IMM)
UR - http://www.scopus.com/inward/record.url?scp=85192946297&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-1103-1_11
DO - 10.1007/978-981-97-1103-1_11
M3 - 会议稿件
AN - SCOPUS:85192946297
SN - 9789819711024
T3 - Lecture Notes in Electrical Engineering
SP - 118
EP - 128
BT - Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 7
A2 - Qu, Yi
A2 - Gu, Mancang
A2 - Niu, Yifeng
A2 - Fu, Wenxing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
Y2 - 9 September 2023 through 11 September 2023
ER -