TY - GEN
T1 - Overbounding GNSS/INS Integration with Uncertain GNSS Gauss-Markov Error Parameters
AU - Crespillo, Omar Garcia
AU - Joerger, Mathieu
AU - Langel, Steve
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - The integration of GNSS with Inertial Navigation Systems (INS) has the potential to achieve high levels of continuity and availability as compared to standalone GNSS and therefore to satisfy stringent navigation requirements. However, robustly accounting for time-correlated measurement errors is a challenge when designing the Kalman filter (KF) used for GNSS/INS coupling. In particular, if the error processes are not fully known, the KF estimation error covariance can be misleading, which is problematic in safety-critical applications. In this paper, we design a GNSS/INS integration scheme that guarantees upper bounds on the estimation error variance assuming that measurement errors are first-order Gauss-Markov processes with parameters only known to reside within pre-established bounds. We evaluate the filter performance and guaranteed estimation by covariance analysis for a simulated precision approach procedure.
AB - The integration of GNSS with Inertial Navigation Systems (INS) has the potential to achieve high levels of continuity and availability as compared to standalone GNSS and therefore to satisfy stringent navigation requirements. However, robustly accounting for time-correlated measurement errors is a challenge when designing the Kalman filter (KF) used for GNSS/INS coupling. In particular, if the error processes are not fully known, the KF estimation error covariance can be misleading, which is problematic in safety-critical applications. In this paper, we design a GNSS/INS integration scheme that guarantees upper bounds on the estimation error variance assuming that measurement errors are first-order Gauss-Markov processes with parameters only known to reside within pre-established bounds. We evaluate the filter performance and guaranteed estimation by covariance analysis for a simulated precision approach procedure.
KW - ARAIM
KW - Colored Noise
KW - GNSS
KW - Gauss Markov Process
KW - Guaranteed estimation
KW - Inertial Systems
KW - Kalman filtering
KW - Overbounding
KW - Precision Approach
UR - http://www.scopus.com/inward/record.url?scp=85087052209&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087052209&partnerID=8YFLogxK
U2 - 10.1109/PLANS46316.2020.9109874
DO - 10.1109/PLANS46316.2020.9109874
M3 - Conference contribution
AN - SCOPUS:85087052209
T3 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
SP - 481
EP - 489
BT - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
Y2 - 20 April 2020 through 23 April 2020
ER -