Performance Limits and Benefits of Adaptive Autoregressive Kalman Filters for GNSS Scintillation-Robust Carrier Tracking

The expansion of Global Navigation Satellite Systems (GNSS) to safety-critical applications in Equatorial and high-latitude regions has unveiled the need to cope with the so-called ionospheric scintillation, an impairment introducing rapid power and carrier phase fluctuations onto the received signal. At carrier tracking level, the use of hybrid autoregressive Kalman filter (KF-AR)-based techniques has shown great potential in mitigating its impact onto the performance of GNSS receivers. In this paper we provide a deep analysis for Kalman filter designers to have a clear idea on the interplay of the Kalman modeling parameters onto the steady-state behaviour of these techniques. To this end, we employ the Bayesian Cramér-Rao bound (BCRB) as a useful tool to predict the expected performance of KF-AR techniques in a straightforward manner. Furthermore, we evaluate the goodness of these techniques under stringent working conditions, where the BCRB analysis is further complemented with empirical results, and we show the importance of using adaptive KF-AR implementations to attain optimal performance.