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

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

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

Atmospheric Air Density Admittance Function for High Precision Gravity Reduction

appear on pages 2-14