Emanuela Falletti

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

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.

Satellite Radiolocalization From GPS to GNSS and Beyond: Novel Technologies and Applications for Civil Mass Market

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.

SALICE project: Satellite-Assisted Localization and Communication Systems for Emergency Services

Restoring the connectivity in the emergency areas and providing NAV/COM services able to support and coordinate the rescue teams represent two of the main telecommunication needs for efficient emergency situation management. The SALICE (Satellite-Assisted LocalIzation and Communication system for Emergency services) Project aimed at identifying the system architecture and the most suitable solutions to be adopted in the future integrated reconfigurable NAV/COM systems and to analyze their feasibility in realistic emergency scenarios. The article analyzes the proposed strategies and the most significant project results in pursuing both the global coverage of the emergency areas and the development of a reconfigurable and cooperative NAV/COM system.

Low Complexity Carrier-to-Noise Ratio Estimators for GNSS Digital Receivers

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.

Theoretical analysis and tuning criteria of the Kalman filter-based tracking loop

AbstractIn recent years, Kalman filter (KF)-based tracking loop architectures have gained much attention in the Global Navigation Satellite System field and have been widely investigated due to its robust and better performance compared with traditional architectures. However, less attention has been paid to the in-depth theoretical analysis of the tracking structure and to the effects of Kalman tuning. A new approach is proposed to analyze the KF-based tracking loop. A control system model is derived according to the mathematical expression of the Kalman system. Based on this model, the influence of the choice of the setting parameters on the temporal evolution of the system response is discussed from the perspective of a control system. As a result, a reasoned and complete suite of criteria to tune the initial error covariance as well as the process and measurements noise covariances is demonstrated. Furthermore, a strategy is presented to make the system more robust in higher order dynamics without degrading the accuracy of carrier phase and Doppler frequency estimates.

Analysis and modelling of MEMS inertial measurement unit

Thanks to advances in the development of Micro-Electromechanical Systems (MEMS), it has been possible to fabricate small dimension and cheap accelerometers and gyros, which are being used in many applications where the GPS/INS integration is carried out, as for example to identify track defects, navigation, geo-referencing, agriculture, etc. Although these MEMS devices have a low-cost, they present different errors which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modelling of these errors is necessary in order to improve the system performance. In this work, Allan Variance and Power Spectral Density techniques are used to identify the random processes that affect the inertial sensor data. Once the random components are identified, they are modelled using first-order Gauss-Markov and random walk processes. Two models are assessed augmenting the states of the Extended Kalman Filter (EKF) to 6 and 9. Subsequently, another analysis and modelling of the inertial sensors which combines Autoregressive Filters and Wavelet De-noising is implemented and in this case the EKF of the loosely coupled GPS/INS integration strategy is augmented with 6, 12 and 18 states. Finally, the results show a comparison between these sensor error models with real data under GPS outage conditions.

A novel local integrity concept for GNSS receivers in urban vehicular contexts

This paper proposes a novel concept of “local integrity” suitable to Global Navigation Satellite System (GNSS) receivers in urban vehicular scenarios. The idea is to take into account not only the system, but also the environment nearby the receiver in its nominal conditions, exploiting the potentialities offered by a Vehicular Ad-hoc Network (VANET) infrastructure. In detail, the potential availability of multiple observations of GNSS signals, taken by different vehicles participating to a VANET, can be shared and combined in order to implement a collaborative spatial/temporal characterization and prediction of the local degradations of the GNSS signals. These concepts are intended to pave the way for the reconsideration/redefinition of the classic GNSS integrity concept, in order to overcome the major problems and limitations to its applicability in urban vehicular scenarios. The analytical development of the proposed methodology and the suitable network architecture for its implementation, as well as some validation results, are presented and discussed in the paper.

Practical implementation and performance assessment of an Extended Kalman Filter-based signal tracking loop

In this paper, the structure of a tracking loop with Extended Kalman Filter (EKF) is analyzed. Particular emphasis is given to the NCO update rule, which is seldom mentioned or studied in previous literature. Furthermore, the structure of an EKF-based software receiver is proposed including the special modules dedicated to the initialization and maintenance of the tracking loop. The EKF-based tracking architecture has been compared with a traditional one based on an FLL/PLL+DLL architecture, and the benefit of the EKF within the tracking stage has been evaluated in terms of final positioning accuracy. Further tests have been carried out to compare the Position-Velocity-Time (PVT) solution of this receiver with the one provided by two commercial receivers: a mass-market GPS module (Ublox LEA-5T) and a professional one (Septentrio PolaRx2e@). The results show that the accuracy in PVT of the software receiver can be remarkably improved if the tracking is designed with a proper EKF architecture and the performance we can achieve is even better than the one obtained by the mass market receiver, even when a simple one-shot least-squares approach is adopted for the computation of the navigation solution.

Assessment on low complexity C/No estimators based on M-PSK signal model for GNSS receivers

In a GNSS receiver the measure of the carrier to noise power density ratio (CNo) is not only a measure of the strength of the received signal, but is also used to determine the lock condition of the carrier and tracking loops and to control the channel scheduling. This paper investigates the possibility of implementing various low complexity C/No estimators, properly adapted from the digital communications world in a real GPS/Galileo receiver. After a theoretical analysis, the paper presents the performance evaluation carried out on different C/No estimation techniques, through both simulated and real GPS signals. We used a real-time GPS/Galileo software receiver, that guaranteed a high level of flexibility and helped to analyze and compare the C/No estimators under test.

Fast Nearly ML Estimation of Doppler Frequency in GNSS Signal Acquisition Process

It is known that signal acquisition in Global Navigation Satellite System (GNSS) field provides a rough maximum-likelihood (ML) estimate based on a peak search in a two-dimensional grid. In this paper, the theoretical mathematical expression of the cross-ambiguity function (CAF) is exploited to analyze the grid and improve the accuracy of the frequency estimate. Based on the simple equation derived from this mathematical expression of the CAF, a family of novel algorithms is proposed to refine the Doppler frequency estimate with respect to that provided by a conventional acquisition method. In an ideal scenario where there is no noise and other nuisances, the frequency estimation error can be theoretically reduced to zero. On the other hand, in the presence of noise, the new algorithm almost reaches the Cramer-Rao Lower Bound (CRLB) which is derived as benchmark. For comparison, a least-square (LS) method is proposed. It is shown that the proposed solution achieves the same performance of LS, but requires a dramatically reduced computational burden. An averaging method is proposed to mitigate the influence of noise, especially when signal-to-noise ratio (SNR) is low. Finally, the influence of the grid resolution in the search space is analyzed in both time and frequency domains.