No abstract

In this work, the performance of the multi-GNSS (Global Navigation Satellite System) Precise Point Positioning (PPP) technique, in static mode, is analyzed. Specifically, GPS (Global Positioning System), GLONASS, and Galileo systems are considered, and quantifying the Galileo contribution is one of the main objectives. The open source software RTKLib is adopted to process the data, with precise satellite orbits and clocks from CNES (Centre National d’Etudes Spatiales) and CLS (Collecte Localisation Satellites) analysis centers for International GNSS Service (IGS). The Iono-free model is used to correct ionospheric errors, the GOT-4.7 model is used to correct tidal effects, and Differential Code Biases (DCB) are taken from the Deutsche Forschungsanstalt für Luftund Raumfahrt (DLR) center. Two different tropospheric models are tested: Saastamoinen and Estimate ZTD (Zenith Troposhperic Delay). For the proposed study, a dataset of 31 days from a permanent GNSS station, placed in Palermo (Italy), and a dataset of 10 days from a static geodetic receiver, placed nearby the station, have been collected and processed by the most used open source software in the geomatic community. The considered GNSS configurations are seven: GPS only, GLONASS only, Galileo only, GPS+GLONASS, GPS+Galileo, GLONASS+Galileo, and GPS+GLONASS+Galileo. The results show significant performance improvement of the GNSS combinations with respect to single GNSS cases.

This paper provides a new adaptive weather routing model, based on the Dijkstra shortest path algorithm, aiming to select the optimal route that maximizes the ship performances in a seaway. The model is based on a set of ship motion-limiting criteria and on the weather forecast maps, providing the sea state conditions the ship is expected to encounter along the scheduled route. The new adaptive weather routing model is applied to optimize the scheduled route in the Northern Atlantic Ocean of the S175 containership, assumed as a reference vessel, based on the weather forecast data provided by the Global WAve Model (GWAM). In the analysis, both wave and combined wind/swell wave conditions are embodied to investigate the incidence on the optimum route assessment. Furthermore, the effect of the vessel speed on the optimum route detection is also investigated. Current results clearly show that it is possible to achieve appreciable improvements, up to 50% of the ship seakeeping performances, without excessively increasing the route length and the voyage duration.

Different positioning techniques have been largely adopted for maritime applications that require high accuracy kinematic positioning. The main objective of the paper is the performance assessment of a Single Point Positioning algorithm (SPP), with a Kalman filter (KF) estimator, adapted for maritime applications. The KF has been chosen as estimation technique due to the ability to consider both the state vector dynamic and the measurements. Particularly, in order to compute an accurate vertical component of the position, suitable for maritime applications, the KF settings have been modified by tuning the covariance matrix of the process noise. The algorithm is developed in Matlab environment and tested using multi-GNSS single-frequency raw data, collected by a smartphone located on board a moving ship. The algorithm performance evaluation is carried out in position domain and the results show an enhancement of meter order on vertical component compared to the classical SPP based on Least Square estimation technique. In addition, different GNSSs configurations are considered to verify the benefits of their integration in terms of accuracy, solution availability and geometry.

No abstract