Geodetic inference: a selection of some challenging topics

In this presentation a kaleidoscopic overview of some geodetic inferential challenges and opportunities will be given. The topics addressed are (1) Interferometric inference; (2) Distributive computing; and (3) Predictive quality. They represent samples of fertile grounds for the typical researcher (PhD student and Postdoc alike) interested in geodetic data processing and modelling, and eager to take up a difficult challenge and/or looking for research opportunities that can make a difference.

Interferometric inference: Although considerable advances have been made in this field, particularly through the very successful global research in interferometric-GNSS, important challenges posed by our mixed-integer models remain. These challenges will be discussed, with a particular reference to distributional multimodality and integer-estimability of FDMA and LTE based carrier-phase systems.

Distributed computing: With data growth numerically straining conventional centralized approaches, complementary cooperative inferential capabilities are asked for. The opportunities of such principles are discussed and examples will be given of dividing estimation problems into easier-to-solve nodal problems which are then coordinated towards an improved, ideally optimal, solution by means of iterative schemes.

Predictive quality: As parameter estimation and statistical testing are typically combined in any geodetic inference, their interactions are to be taken into account when describing the quality of one’s model predictions. The challenges and intricacies that this brings are highlighted, whereby it is suggested that several of the existing validation and representation procedures need revisiting to ensure suitability of their quality descriptions.