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Today, most of the aircrafts are navigated by global navigation satellite systems (GNSSs). Landing is a dangerous phase of a flight especially when an airport runway is not clearly seen from the aircrafts. In such cases, GNSSs are useful for a safe landing under the circumstances that healthy signals, free of any interference, reach to GNSSs receiver antennas mounted on the aircrafts. This shows the importance of establishing GNSS interference localisation security networks around airports. Designing a good configuration for the points with GNSS antennas at for receiving interference signals is important for a successful localisation of the interference device. Here, the time-difference of the arrivals of an interference signal to such points or anchor nodes (ANs), are used as observables, and a security network with four ANs is optimally designed along the runways of the Arlanda airport to reduce the dilution of precision (DOP) of the network. Our study showed that by such an optimisation, the maximum DOP value can reduce by 50% meaning a significant increase in the probability of a successful interference device localisation.

The book “Physical Geodesy” by Martin Vermeer is written in a proper pedagogic way with a simple and complete text, good examples, conceptual questions and excellent exercises at the end of each chapter. This book is a suitable reference for educational purposes in Physical Geodesy from the bachelor to higher levels of education.

The NKG2015 geoid model covers the Nordic and Baltic countries and has been computed based on the least-squares modification of Stokes’ formula with additive corrections method. New and precise terrestrial, airborne and shipborne gravimetric measurements, the recent global gravity model of the gravity field and steady-state ocean circulation explorer (GOCE) and detailed digital terrain models over each territory have been used for computing this new geoid model. Some estimates for the error of this model have been roughly presented by comparing it with the global navigation satellite system (GNSS) data over each country. In this paper, our goal is to have a closer look at the relative error of this model by performing some statistical tests and finding the proper corrective surface for absorbing the systematic errors over each country. Our main assumption is realisticity of the errors of GNSS/levelling data and we will investigate its consequences in estimating the error of the geoid model. Our results show that the 4-parameter corrective surface is suitable for modelling the systematic trends of the differences between the gravimetric and GNSS geoid heights in Sweden, Denmark and Finland, but a filtered discrepancies by a confidence interval of 95% should be used for Sweden. A 7-aparameter model is suitable for the filtered discrepancies with the confidence interval of 95% in Norway. Based on the selected corrective surface and our newly developed regional iterative variance estimator, the confidence interval for the error of NKG2015 geoid model in Sweden, Denmark and Norway yielded 0-6.5 mm, 1.8-5.2 mm, 14.8-17.7 mm, respectively with a confidence level of 95%. We could not estimate the geoid error in Finland because the given error of the GNSS/levelling heights is significantly larger than the size of residuals. Based on the selected corrective surfaces and our presented local variance estimator, the average error of geoid becomes 3.6, 2.4, 8.8 and 5.8 mm with a confidence interval of 68%, respectively, over Sweden, Denmark, Norway and Finland.

Monitoring deformation of man-made structures is very important to prevent them from a risk of collapse and save lives. Such a process is also used for monitoring change in historical objects, which are deforming continuously with time. An example of this is the Vasa warship, which was under water for about 300 years. The ship was raised from the bottom of the sea and is kept in the Vasa museum in Stockholm. A geodetic network with points on the museum building and the ship’s body has been established and measured for 12 years for monitoring the ship’s deformation. The coordinate time series of each point on the ship and their uncertainties have been estimated epoch-wisely. In this paper, our goal is to statistically analyse the ship’s hull movements. By fitting a quadratic polynomial to the coordinate time series of each point of the hull, its acceleration and velocity are estimated. In addition, their significance is tested by comparing them with their respective estimated errors after the fitting. Our numerical investigations show that the backside of the ship, having highest elevation and slope, has moved vertically faster than the other places by a velocity and an acceleration of about 2 mm/year and 0.1 mm/year2, respectively and this part of the ship is the weakest with a higher risk of collapse. The central parts of the ship are more stable as the ship hull is almost vertical and closer to the floor. Generally, the hull is moving towards its port and downwards