# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 1 Introduction  
Ionospheric scintillation can lead to a variety of effects on the Global Navigation Satellite System (GNSS), including cycle slips, measurement errors and even loss of lock on satellites, which reduces the number of available satellites tracked by GNSS receivers. The geomagnetic storm is one of the main causes of scintillation [1, 2]. According to [PERSON] et al. [3], more than 70% of cycle slips were caused by strong scintillation. Furthermore, the phase lock loop (PLL) cannot recover after a long time period of cycle slips under strong scintillation, which leads the baseband signal power to decrease by more than 13 dB [4]. Thus, the PLL frequency is further detuned and as a consequence the signal is completely lost. Even worse, such conditions cannot be avoided under strong scintillation, even when using high-grade GNSS receivers. Therefore, the satellite geometry is degraded or the positioning may even fail due to having fewer than four satellites in view. Thus,mitigation of the ionospheric scintillation effects is important, especially in equatorial and auroral areas. A variety of methods have been proposed to mitigate the effect of scintillation on GNSS positioning. The effect of scintillation can be mitigated effectively using these methods. However, most of these methods employed scintillation parameters S4 and \(\sigma_{\Phi}\), which are respectively used to characterize amplitude and phase scintillation [5]. [PERSON] et al. [6] modelled the tracking error variance of GNSS receiver delay locked loop (DLL), based on [PERSON] et al. [7]. The variances were used to weight the pseudorange measurements in the least squares stochastic (LSS) model and the root mean square (RMS) of height was improved by up to 21%. Furthermore, the tracking error variance of the PLL was also applied to weight the carrier phase measurements in the LSS model, where the improvement of height RMS increased to 38% [8]. However, the Conker model is invalid when S4 (L1) exceeds 0.707. Additionally, it is not easy to obtain the spectral strength and slope required for computing the tracking error variance component relevant to the phase scintillation, especially for high frequency signals. Thus, a modified version of the Conker model, named the Conker' model was proposed, where S4' is derived instead of S4 [9]. In the Conker' model, the normalization of the signal-to-noise ratio (SNR) was implemented at each second in S4' instead of each minute in S4 [9]. Using the Conker' model, the positioning performance can be substantially improved under strong scintillation. In addition, [PERSON] et al. [10] applied the \(\alpha\)-\(\mu\) model [11] instead of the Conker model to calculate the tracking error variances of the DLL and PLL; adjustment to the weights of the LSS model improved the 3D RMS by 62-75%. Moreover, [PERSON] et al. [12] managed to detect and exclude scintillation affected satellites during the positioning process, using a technique called receiver autonomous integrity monitoring (RAIM) which substantially improved the resilience of precise point positioning (PPP) to scintillation. Even with these methods, loss of signal lock and cycle slips are still the primary problems under scintillation, which can be severe when using a single satellite constellation. Therefore, it was proposed that the data of GPS and GLONASS could be integrated to obtain more reliable positioning solutions under moderate to strong scintillation [13]. This improved the RMS by 63% in height and 57% in 3D compared to using GPS alone. By applying an integrated novel adaptive architecture named MF-On-ARKF, the RMS error (RMSE) of the light of sight (LOS) phase considerably decreased under both single- and multi-frequency conditions, which improved the synchronization performance [14]. Scintillation parameters can be generated from data with a frequency of at least 1 Hz [15; 16; 17; 18]. Thus, scintillation parameters can be obtained from standard geodetic receivers. However, 1 Hz data is substantially less available than 1/30 Hz data [19].