Bridging GPS Outages Using Spectral Fusion and Neural Network Models in Support of Multibeam Hydrography

In classical hydrographic surveying, the use of GPS is limited to providing horizontal control for survey vessels. More recently, an alternative practice has evolved, which determines depth values relative to a geodetic datum and then relate them to tidal datums through a series of vertical datum transformations. Although it has a number of advantages over classical hydrographic surveying, this practice requires accurate 3D positioning information. Unfortunately, accurate 3D positioning solution may not always be available as a result of communication link problems, GPS outages, or unsuccessful fixing for the ambiguity parameters. This paper examines the use of wavelet analysis to spectrally combine the GPS/INS height data series and the heave signal to bridge the height data gaps. In addition, a neural network-based model is developed to precisely predict the horizontal component of the survey vessel.En los levantamientos hidrográficos clásicos, el uso del GPS está limitado al suministro de control horizontal para los buques hidrográficos. Más recientemente, se ha desarrollado una práctica alterna-tiva, que determina los valores de la profundidad relativos a un datum geodésico y los relaciona pos-teriormente con los datums de mareas a través de una serie de transformaciones del datum vertical. Aunque tiene una serie de ventajas con respecto a los levantamientos hidrográficos clásicos, esta práctica requiere una información precisa del posicionamiento en 3D. Desgraciadamente, puede que una solución de posicionamiento preciso en 3D no esté siempre disponible como resultado de los problemas de enlaces de datos, los cortes GPS, o un ajuste infructuoso de parámetros de ambigüedad. Este artículo examina el uso de un análisis de ondas pequeñas para combinar espectralmente la serie de datos de altura GPS/INS y la señal de oleaje para superar las deficiencias de datos de alturas. Además, se ha desarrollado un modelo basado en la red neural para predecir con precisión la compo-nente horizontal del buque hidrográfico.Dans les levés hydrographiques classiques, l‘utilisation du GPS est limitée à la fourniture d‘un contrôle horizontal pour les bâtiments hydrographiques. Plus récemment, une autre pratique est ap-parue et celle-ci détermine les valeurs de profondeur par rapport à un système géodésique puis les rapporte au niveau de référence des marées par le biais d‘une série de transformations du système géodésique vertical. Bien que ceci offre un certain nombre d‘avantages par rapport aux levés hydro-graphiques classiques, cette pratique nécessite des informations exactes sur la détermination de la position en 3D. Malheureusement, une solution exacte de détermination de la position en 3D n‘est pas toujours disponible à cause de problèmes de liaison en matière de communications, de défaillan-ces GPS, ou de réparation infructueuse des paramètres d‘ambiguïté. Le présent article examine l‘uti-lisation d‘une analyse des ondelettes pour combiner de manière spectrale les séries de données de hauteur GPS/INS et le signal de pilonnement afin de combler les lacunes en données de hauteur. En outre, un modèle inspiré d‘un réseau neuronal est en cours de développement en vue d‘une prédic-tion précise de la composante horizontale du bâtiment hydrographique

LIDAR SLAM-AIDED VEHICULAR NAVIGATION SYSTEM FOR GNSS-DENIED ENVIRONMENTS

Typically, in situations where Global Navigation Satellite System (GNSS) signals are unavailable, navigation systems rely on integrating GNSS and inertial navigation system (INS) data. While such integration can provide accurate positioning during short GNSS signal outages, it cannot sustain prolonged GNSS outages. The reason for this is that the system's performance depends solely on the INS and can result in significant errors over time. To address this issue, additional onboard sensors are necessary. This study proposes a navigation system that integrates INS and LiDAR simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF). The system was tested using the raw KITTI dataset in various outdoor driving scenarios without GNSS signals. It is shown that the proposed system significantly outperformed the INS-only system, with an average RMSE improvement of around 93% and 58% in the horizontal and the up directions, respectively

Temporal shoreline series analysis using GNSS

In recent decades, Boa Viagem beach located in the city of Recife-PE and Piedade in Jaboatão dos Guararapes-PE (Brazil) has seen urbanization near the coastline causing changes in social, economic and morphological aspects, where coastal erosion problems are observed. This study uses GNSS (global navigation satellite system) shoreline monitoring approach, which is quicker, and provides continuously updatable data at cm-level accuracy to analyze and determine temporal positional shifts of the shoreline as well as annual average rates through EPR (end point rate). To achieve this, kinematic GNSS survey data for the years 2007, 2009, 2010 and 2012 were used. The results show sectorial trends over the years, with the highest annual retreat rate of 8.16 m /year occurring during the period 2007-2009. Variety of different patterns over the shoreline were also observed. These findings could be essential for decision making in coastal environments

Research Article. Improved Dual Frequency PPP Model Using GPS and BeiDou Observations

This paper introduces a new dual-frequency precise point positioning (PPP) model, which combines GPS and BeiDou observations. Combining GPS and BeiDou observations in a PPP model offers more visible satellites to the user, which is expected to enhance the satellite geometry and the overall PPP solution in comparison with GPSonly PPP solution. However, combining different GNSS constellations introduces additional biases, which require rigorous modelling, including GNSS time offset and hardware delays. In this research, ionosphere-free linear combination PPP model is developed. The additional biases, which result from combining the GPS and BeiDou observables, are lumped into a new unknown parameter identified as the inter-system bias. Natural Resources Canada’s GPSPace PPP software is modified to enable a combined GPS/BeiDou PPP solution and to handle the newly introduced biases. A total of four data sets at four IGS stations are processed to verify the developed PPP model. Precise satellite orbit and clock products from the IGS-MGEX network are used to correct both of the GPS and BeiDou measurements. It is shown that a sub-decimeter positioning accuracy level and 25% reduction in the solution convergence time can be achieved with combining GPS and Bei-Dou observables in a PPP model, in comparison with the GPS-only PPP solution

Enhanced model for precise point positioning with single and dual frequency GPS/Galileo observables

This paper introduces a newly developed model for both single and dual-frequency precise point positioning (PPP), which combines GPS and Galileo observables. As is well known, a drawback of a single GNSS system is the availability of sufficient number of visible satellites in urban areas. Combining GPS and Galileo systems offers more visible satellites to users, which is expected to enhance the satellite geometry and the overall positioning solution. However, combining GPS and Galileo observables introduces additional biases which require rigorous modelling, including the GPS to Galileo time offset (GGTO) and the inter-system bias. This research introduces a new ionosphere-free linear combination model for GPS/Galileo PPP, which accounts for the additional errors and biases. An additional unknown is introduced in the least-squares estimation model to account for the additional biases of the GPS/Galileo PPP solution. It is shown that a sub-decimeter level positioning accuracy and 20% reduction in the solution convergence time can be achieved with the newly developed GPS/Galileo PPP model

Non-Linear Filtering for Precise Point Positioning GPS/INS integration

This research investigates the performance of non-linear estimation filtering for GPS-PPP/MEMS-based inertial system. Although integrated GPS/INS system involves nonlinear motion state and measurement models, the most common estimation filter employed is extended Kalman filter. In this paper, both unscented Kalman filter and particle filter are developed and compared with extended Kalman filter. Tightly coupled mechanization is adopted, which is developed in the raw measurements domain. Un-differenced ionosphere-free linear combination of pseudorange and carrier-phase measurements is employed. The performance of the proposed non-linear filters is analyzed using real test scenario. The test results indicate that comparable accuracy-level are obtained from the proposed filters compared with extended Kalman filter in positioning, velocity and attitude when the measurement updates from GPS measurements are available