Integrity Monitoring of Integrated Satellite/Inertial Navigation Systems Using the Likelihood Ratio
1996 (English)Report (Other academic)
Global Navigation Satellite Systems (GNSS) have the ability to fulfill the navigation accuracy requirements of most applications. The systems do however lack continuity and integrity to meet the requirements of high precision navigation applications. The use of a combination of Inertial Navigation Systems (INS) and GNSS information do however show promising results in fulfilling these requirements. Methods for monitoring the integrity of integrated INS-GNSS systems are investigated. Integration of INS and GNSS is usually accomplished using a Kalman filter for recursive estimation of the parameters of interest. The residual used for integrity monitoring is the Kalman filter innovation. The innovation signatures of different types of faults are analyzed. Since two of the most likely types of faults in an integrated solution are INS sensor bias shifts and satellite range bias drifts or jumps, these additive types of changes are studied in more detail. Taking the approach of hypothesis testing of the two hypotheses unfailed and failed system, fault detection methods based on the likelihood ratio are considered and the Generalized Likelihood Ratio (GLR) test is proposed to be used. This method uses the innovations of the Kalman filter to compute the maximum likelihood estimates of the time and magnitude of an assumed change. Using these estimates, it evaluates the log-likelihood ratio of a change versus no change. The GLR test uses a linearly in time increasing number of mat- ched filters. Different ways of decreasing this computational burden are discussed, showing that fast detection can be achieved even with a small and constant number of matched filters. A further advantage of the GLR test is that in addition to detecting the occurrence of a fault, it also estimates its magnitude, direction and time of occurrence, making it possible to identify the source of the fault, exclude faulty satellites and correct the Kalman filter estimate without reprocessing the affected data.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 1996. , 10 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1898
Global navigation Satellite systems, Inertial navigation systems, Kalman filter, Generalized likelihood ratio
IdentifiersURN: urn:nbn:se:liu:diva-55364ISRN: LiTH-ISY-R-1898OAI: oai:DiVA.org:liu-55364DiVA: diva2:316019