Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time.

Coupled amplitude delay lock loops (CADLL) is a recently proposed multipath estimation and mitigation technique based on joint estimation of line-of-sight (LOS) and multipath signal amplitude, code phase, and carrier phase. The CADLL performance is evaluated against two widely known multipath mitigation methods: the high-resolution correlator (HRC), representative of the correlators combination methods, and the multipath mitigation technique (MMT), representative of multipath estimation methods. Multiple tests emulating various scenarios are performed to demonstrate that CADLL always generates better results than the other two methods. Additionally, CADLL has better noise performance, can estimate multipath signals using shorter integration time, and is capable of tracking dynamic multipath signals. Simulation tests using a statistical urban multipath signal model prove that CADLL is effective in estimating and mitigating multipath in severe multipath environments. These simulation results are further validated using satellite signals generated by Spirent Global Navigation Satellite System (GNSS) 6700.

In early September 2017, several space weather events triggered disturbed conditions of the near‐Earth space. The combination of two coronal mass ejection arrivals, associated with an X‐class flare, caused a strong geomagnetic storm on 7 and 8 September, thus inducing diffuse ionospheric phase scintillations on Global Navigation Satellite System (GNSS) signals. This work analyzes the effects and the actual impact of such phase scintillations on transionospheric Global Positioning System (GPS) signals and on related positioning accuracy. The research focuses in particular on high‐latitude GPS L1 data, recorded during a test campaign in Svalbard, Norway. The joint effect of satellites at low elevation and the exposure of ionosphere to the geospace forcing make navigation a critical task for such a challenging environment. Data analysis shows that the performance of carrier smoothing algorithms was affected by the presence of moderate and strong phase scintillation. It is shown in this study that positioning errors double when GPS signals affected by scintillation are used. This work shows that scintillations induce a considerable clustering effect on the smoothed positioning solutions; therefore, a methodology to automatically and autonomously detect the boundaries of the scintillation event is suggested according to such an high‐level effect. The use of software‐defined radio receivers for automatically capturing and processing GNSS data affected by scintillation is an added value to the analysis, as it offers the possibility to implement advanced signal processing techniques and a deeper observation of the impact of scintillations on the signals.