TY - JOUR
T1 - Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation
AU - Li, Linfeng
AU - Wang, Jian
AU - Chen, Zhiming
AU - Yu, Teng
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/10
Y1 - 2023/10
N2 - In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.
AB - In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.
KW - adaptive and robust Kalman filter
KW - global positioning system (GPS)
KW - maximum correntropy criterion (MCC)
KW - sliding window method
KW - strapdown inertial navigation system (SINS)
UR - http://www.scopus.com/inward/record.url?scp=85174050551&partnerID=8YFLogxK
U2 - 10.3390/s23198131
DO - 10.3390/s23198131
M3 - Article
C2 - 37836960
AN - SCOPUS:85174050551
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 19
M1 - 8131
ER -