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As more and more attention is received by the underlying passive potential of Global Navigation Satellite Systems (GNSS) signals, reflectometry and scatterometry are visited in this paper in order to assess water surface identification and aircraft altimetry performance. This is possible thanks to the high reflectivity of Global Positioning System (GPS) signals in the L-Band frequency range on water as well as ice and snow covered surfaces which compensates for the otherwise low signal intensity. Indeed, GNSS reflectometry belongs to the class of bistatic radar systems where transmitter and receiver reside at different locations and signals pass through a reflecting surface. Code phase and carrier phase based time delay measurements between the direct path and reflected signals, are used to determine the small aircraft's height with respect to the reflecting surface. On the other hand, specular reflection points are computed, and a comparison of the reflected power to the direct received power is shown to yield a good water surface identification performance. Code and carrier phase measurements are implemented in this paper to obtain cm level height estimates.

On the 20th of October 2011, the first two satellites of the Galileo constellation have been launched successfully. The possibility to have these two satellites in orbit represented a great opportunity for researchers to receive and monitor the first Galileo signals. After the satellite switch-on, we continuously monitored the signal broadcast by both the satellites, using a software radio receiver. This paper shows the analysis on the navigation message that was broadcast by the IOV-PFM Galileo satellite on the E1 bandwidth, starting from December 2011. In particular, we were able to observe a valid ephemeris data on December 21st and December 22nd. The successful interpretation of the navigation message enabled the first GPS+Galileo experimentations using real signals that were performed in static condition, through code-phase measurements.

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 Global Navigation Satellite System (GNSS) receiver is able to compute the user position through a trilateration procedure, which includes the measure of the distance between the receiver and a set of satellites. Two different approaches are tipically used and implemented in commercial receivers. The former relies on code tracking, the latter leverages carrier phase measurements performed during carrier tracking.

The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramer-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.

Many navigation problems involve constraints. For example, motor vehicle travel tends to be constrained by road and building boundaries. We consider filtering problems where censuring functions can be used to constrain the measurements. It is established that the ensuing filtered estimates are unbiased, provided that the underlying probability density functions are even and the censuring functions are odd. A loosely-coupled GPS and inertial navigation system (INS) application is discussed in which an integrated solution exhibits improved performance during GPS outages when a priori information is used to constrain the altitude measurements

The convergence of an expectation-maximization (EM) algorithm for state matrix estimation is investigated. It is shown for the expectation step that the design and observed error covariances are monotonically dependent on the residual error variances. For the maximization step, it is established that the residual error variances are monotonically dependent on the design and observed error covariances. The state matrix estimates are observed to be unbiased when the measurement noise is negligible. A navigation application is discussed in which the use of estimated parameters improves filtering performance.

The European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) has been developed by the European Union to enhance safety and efficiency of the railway transportation. So far, the positioning and monitoring of the trains have been based mainly on trackside equipment named balises: the introduction of Global Navigation Satellite Systems (GNSS) to implement an absolute positioning system on trains could lead to a better utilization of the line with a consequent line capacity increase, along with reliability increasing and cost reducing due to the removal of expensive infrastructures. This paper describes the insertion of a state-of-the-art channel model, named Land Mobile Satellite Channel Model (LMSCM) properly adapted to represent railways scenarios, into a hardware-in-the-loop system, where the parameters generated by LMSCM are used to configure an RF GNSS signal generator. The use of the hardware generator eases the simulation of the entire GNSS constellation, while the availability of the RF signal enables the assessment of the effects of rail-domain multipath on commercial receivers. The resulting model, named Rail-Land Mobile Satellite Channel Model (Rail-LMSCM), has been enhanced with the support of different kind of scenarios a train may encounter around the rail track, as well as with the introduction of GNSS outages due to the presence of tunnels. Such enhancements make the Rail-LMSCM a more realistic average representation of the railway environment. The implemented Rail-LMSCM has been used to test in the lab the local propagation effects on the performance of a complete GNSS receiver.