TY - GEN
T1 - A Fuzzy Based Parallel Filtering Matching Algorithm for Gravity Aided Navigation
AU - Zhao, Maosu
AU - Miao, Lingjuan
AU - Shao, Haijun
AU - Dai, Tian
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Serial observed gravity anomaly data and a gravity anomaly database map can be used to correct the errors of inertial navigation system based on EKF. Considering a disadvantage in EKF based matching algorithm, low matching accuracy when gravity gradient anomaly along longitude or latitude direction is too small, a fuzzy based parallel filtering matching algorithm is proposed in this paper. Instead of assigning the same weights to fitting points in stochastic linearization process, the fuzzy theory is introduced to assign optimum weights to fitting points. Besides, a bank of parallel Kalman filters are designed to guarantee the robustness of the proposed algorithm. Simulation results in different matching areas show the effectiveness of the proposed algorithm. Compared with the traditional EKF based matching algorithm, the proposed algorithm can provide higher matching accuracy.
AB - Serial observed gravity anomaly data and a gravity anomaly database map can be used to correct the errors of inertial navigation system based on EKF. Considering a disadvantage in EKF based matching algorithm, low matching accuracy when gravity gradient anomaly along longitude or latitude direction is too small, a fuzzy based parallel filtering matching algorithm is proposed in this paper. Instead of assigning the same weights to fitting points in stochastic linearization process, the fuzzy theory is introduced to assign optimum weights to fitting points. Besides, a bank of parallel Kalman filters are designed to guarantee the robustness of the proposed algorithm. Simulation results in different matching areas show the effectiveness of the proposed algorithm. Compared with the traditional EKF based matching algorithm, the proposed algorithm can provide higher matching accuracy.
KW - Fuzzy theory
KW - Gravity aided inertial navigation system
KW - Kalman filter
KW - Multiple model adaptive estimation
UR - http://www.scopus.com/inward/record.url?scp=85072402107&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816218
DO - 10.1109/ICMA.2019.8816218
M3 - Conference contribution
AN - SCOPUS:85072402107
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 933
EP - 938
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
Y2 - 4 August 2019 through 7 August 2019
ER -