TY - GEN
T1 - A Hybrid Method for INS/GPS Integrated Navigation System Based on the Improved Karman Filter and Back Propagation Neural Network
AU - Hu, Mutian
AU - Song, Tao
AU - Ye, Jianchuan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The navigation system for unmanned aerial vehicle is commonly integrated by the inertial navigation system (INS) and global positioning system (GPS). Nevertheless, errors of INS/GPS accumulate over time when GPS suffers from outages. In this paper, a comprehensive filter called MH∞-5thCKF is proposed for taking the place of Karman filter (KF). It combines superiorities of H-infinity filter and multiple fading filter to enhance filtering precision and robustness. In addition to MH∞-5thCKF, an optimized back propagation neural network (BPNN) is utilized for GPS outages. The ELSHADE-SPACMA algorithm is introduced to select BPNN parameters. When satellite signals are available, the improved BPNN uses angular rates, specific forces and GPS increments to train the model. Once satellite signals are lost, the improved BPNN predicts pseudo-GPS information so that MH∞-5thCKF continues compensating INS errors. Compared with the conventional KF and BPNN, simulation results demonstrate that proposed algorithms not only enhance filtering performance, but also avoid BPNN falling into local optimum to guarantee model stability when GPS fails to work.
AB - The navigation system for unmanned aerial vehicle is commonly integrated by the inertial navigation system (INS) and global positioning system (GPS). Nevertheless, errors of INS/GPS accumulate over time when GPS suffers from outages. In this paper, a comprehensive filter called MH∞-5thCKF is proposed for taking the place of Karman filter (KF). It combines superiorities of H-infinity filter and multiple fading filter to enhance filtering precision and robustness. In addition to MH∞-5thCKF, an optimized back propagation neural network (BPNN) is utilized for GPS outages. The ELSHADE-SPACMA algorithm is introduced to select BPNN parameters. When satellite signals are available, the improved BPNN uses angular rates, specific forces and GPS increments to train the model. Once satellite signals are lost, the improved BPNN predicts pseudo-GPS information so that MH∞-5thCKF continues compensating INS errors. Compared with the conventional KF and BPNN, simulation results demonstrate that proposed algorithms not only enhance filtering performance, but also avoid BPNN falling into local optimum to guarantee model stability when GPS fails to work.
KW - back propagation neural network
KW - differential evolution
KW - GPS outages
KW - INS/GPS integrated navigation
KW - Kalman filter
UR - http://www.scopus.com/inward/record.url?scp=85203813372&partnerID=8YFLogxK
U2 - 10.1109/ICRCA60878.2024.10648989
DO - 10.1109/ICRCA60878.2024.10648989
M3 - Conference contribution
AN - SCOPUS:85203813372
T3 - 2024 8th International Conference on Robotics, Control and Automation, ICRCA 2024
SP - 477
EP - 484
BT - 2024 8th International Conference on Robotics, Control and Automation, ICRCA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Robotics, Control and Automation, ICRCA 2024
Y2 - 12 January 2024 through 14 January 2024
ER -