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.

| Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented.

This paper proposes a methodology for automatic, accurate, and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz postcorrelation data provided by a global navigation satellite system receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts.

We investigate the geospace response to the 2015 St. Patrick's Day storm leveraging on instruments spread over Southeast Asia (SEA), covering a wide longitudinal sector of the low‐latitude ionosphere. A regional characterization of the storm is provided, identifying the peculiarities of ionospheric irregularity formation. The novelties of this work are the characterization in a broad longitudinal range and the methodology relying on the integration of data acquired by Global Navigation Satellite System (GNSS) receivers, magnetometers, ionosondes, and Swarm satellites. This work is a legacy of the project EquatoRial Ionosphere Characterization in Asia (ERICA). ERICA aimed to capture the features of both crests of the equatorial ionospheric anomaly (EIA) and trough (EIT) by means of a dedicated measurement campaign. The campaign lasted from March to October 2015 and was able to observe the ionospheric variability causing effects on radio systems, GNSS in particular. The multiinstrumental and multiparametric observations of the region enabled an in‐depth investigation of the response to the largest geomagnetic storm of the current solar cycle in a region scarcely reported in literature. Our work discusses the comparison between northern and southern crests of the EIA in the SEA region. The observations recorded positive and negative ionospheric storms, spread F conditions, scintillation enhancement and inhibition, and total electron content variability. The ancillary information on the local magnetic field highlights the variety of ionospheric perturbations during the different storm phases. The combined use of ionospheric bottomside, topside, and integrated information points out how the storm affects the F layer altitude and the consequent enhancement/suppression of scintillations.