Todd Walter, Zixi Liu, Juan Blanch, Stanford University; Kristy Pham, John Mick, William Wanner, William J. Hughes FAA Technical Center

View Abstract Sign in for premium content

Abstract:

On September 20, 2020, GPS Space Vehicle Number (SVN) 74 exhibited unusual behavior that appears to have led to anomalous behavior for some GPS users. This paper investigates the observed signals in order to characterize the broadcast signal behavior over the course of the event. Investigations like this are critical to understanding the potential impact of such events on safety-of-life applications using GPS. In particular, Advanced Receiver Autonomous Integrity Monitoring (ARAIM) has associated requirements to monitor signal behavior and predict future performance levels including accuracy and fault probabilities. The first question that arose from the initial reported behavior was whether or not the event constituted a major service failure as defined in the GPS Standard Positioning Service Performance Standard (GPS SPS PS)[1]. As it turns out, the answer was not so easily determined. GPS has many different methods to indicate an alarm to the user, that if employed will successfully indicate to the user that the satellite should not be included in their position solution computation. Among these methods are an absence of a trackable signal and the use of alternative data patterns in the navigation message. A difficulty with determining whether these indicators were used is that they are not always recorded into data archives and may be confused with network outages or other data loss mechanisms. Although the alarm may result in the loss of data, the absence of data does not guarantee the presence of an alarm. We need to be able to rule out other explanations for data loss in the archival records. We therefore need to obtain and scrutinize the measured carrier to noise ratios and raw navigation data bits in order to firmly establish the presence and timing of these different alarm mechanisms. This paper describes the signal behavior observed indicating different health states and also describes the transition characteristics between each state. We will describe some of the reported receiver behaviors and which mechanisms were used to protect users from the potential use of misleading data.