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.

It is known that satellite radiolocalization was born in the military environment and was originally conceived for defense purposes. Nevertheless, the commercial explosion (dated to 20 years ago) of global positioning system (GPS) in the civil market (automotive, tourism, etc.) significantly changed the original perspectives of this technology. Another big change is expected when other global navigation satellite systems (GNSSs) such as the European Galileo or the Chinese COMPASS become operational and commercial. In fact, modern GNSSs are conceived principally for the civil market (at the opposite of GPS, whose civil employment is given as a sort of “kind gift,” with lower performance than that one granted to military users). The scope of this paper is to provide readers with a clear focus about the potentialities of current and forthcoming GNSSs and associated technologies in a renewed mass-market perspective. The paper also opens a window to the future of radiolocalization technology beyond GPS and GNSS, dealing with the role of digital signal processing and software-defined radio (SDR) in next-generation navigation systems and with the seamless integration of satellite-based navigation with other technologies in order to provide reliable position information also in hostile environments.

In a GNSS receiver the measure of C/N₀ is not only supplementary information provided to the user together with the PVT (position, velocity, time), but it is also fundamental information to determine the status of the tracking subsystems and to control the receiver in critical environments. A classic approach to estimate the C/N₀ in a navigation receiver is known as the narrowband-wideband power ratio method (NWPR). We investigate the suitability of different estimation methods, properly adapted from the digital communications world where a wide range of literature is available on the problem of estimating the signal-to-noise power ratio (SNR) of M-PSK modulations in additive white Gaussian noise (AWGN). The objective here is to identify a subset of candidate algorithms for SNR estimation, previously proposed for communications receivers. These methods are compared in specific situations of interest, such as weak signals or high signal-to-noise conditions obtained with high-gain antennas, and considering software defined radio receiver implementations. Simulation campaigns and a set of real GPS-L1 signals are used to test and compare the performance of the selected algorithms in a broad range of C/N₀ values (from 20 to 70 dBHz), where the classic NWPR method is used as an authoritative benchmark. We conclude that a number of low-complexity algorithms for SNR estimation, based on a simple communication signal model, can be tailored to digital GNSS receivers. The performance is comparable with respect to the NWPR approach, but the complexity is reduced and possible improvements can be foreseen using the tested algorithms.

With the fast growing diffusion of the real-time high accuracy applications based on the Global Navigation Satellite System (GNSS), the robustness of the GNSS receiver performance has become a compelling requirement. Disruptive effects can be induced to the signal processing stages of GNSS receivers due to the disturbances from Radio-Frequency Interference (RFI), even leading to a complete outage of the positioning and timing service. A typical RFI threat to the GNSS signals is represented by portable jammers which transmit swept-frequency (chirp) signals in order to span the overall GNSS bandwidth. The implementation in the receivers of Adaptive Notch Filters (ANFs) for chirp cancellation has been extensively investigated and proved to be an efficient countermeasure. However, the performance of ANF is strongly dependent on its configuration setup. Inappropriate parameter settings of the ANF for interference removal may induce severe distortion to the correlation process. In addition, an effective mitigation will still introduce a vestigial signal distortion contributed by the residual unmitigated chirp and the ANF operation itself, being not negligible for high accuracy solutions. This paper addresses the detailed analysis for assessing the effects of interference mitigation by notch filtering. A bias compensation strategy is proposed, wherein for each Pseudo Random Noise (PRN) the biases due to the parameter settings of the notch filter are estimated and compensated. The impact of using the ANF operation on chirp signals at the acquisition and tracking stages of GNSS receivers is analyzed. On the basis of the three proposed metrics, the effects can be quantitatively estimated to depict a complete picture of the most influential parameters of the chirp and the ANF configurations, as well as the optimal achievable performance at the acquisition and tracking stages.