Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics

Abstract

-Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

Improved Method for Single and Multiple GNSS Faults Exclusion based on Consensus Voting

“…For example, McBurney and Brown (1989) and Ren and Ching-Fang (1995) proposed to use a Kalman filter to achieve GPS integrity monitoring. Yoo et al (2012) proposed a new method to achieve GPS fault detection and isolation by comparing the measured pseudorange and the predicted pseudorange information obtained by a pseudorange prediction model. In addition, Yang et al (2014) proposed to use supplementary information provided by external sensors to achieve integrity monitoring.…”