Volume 3, Issue 1

High accuracy 3D representation and monitoring of objects is receiving increasing interest both in science and industrial applications. Up to now tasks like monitoring of building displacements or deformations were solved by means of artificial targets on the objects of interest, although mature optical 3D measurement and laser scanning techniques are available. Such systems can perform their measurements even without targeting. This paper presents a new optical 3D measurement system, based on the fusion between a geodetic image sensor and a laser scanner. The main goal of its development was the automation of the whole measurement process, including the tasks of point identification and measurement, deformation analysis, and interpretation. This was only possible by means of new methods and techniques originally developed in the area of Artificial Intelligence; both point detection and deformation analysis are supported by decision systems that use such techniques. The resulting complex multi-sensor system is able to measure and analyse the deformation of objects, as shown in experiments. In this article we focus on specific key components and novel techniques that have been developed, and briefly report on the current stage of the whole system.

Global Navigation Satellite System (GNSS) is a critical space-borne geospatial infrastructure providing essential positioning supports to a range of location-sensitive applications. GNSS is currently dominated by the US Global Positioning System (GPS) constellation. The next generation GNSS is expected to offer more satellites, better positioning provision, and improved availability and continuity of navigation support. However, GNSS performance in 3-D urban environments is problematic because GNSS signals are either completely blocked or severely degraded by high-rising geographic features like buildings. The aim of this study is to gain an in-depth understanding of the changing spatial patterns of GNSS performance, measured by the number of visible satellites (NVS) and position dilution-of-precision (PDOP), in the urban canyons of Melbourne, Australia. The methodology used includes the following steps: (1) determination of the dynamic orbital positions of current and future GNSS satellites; (2) development of a 3-D urban model of high geometric quality for Melbourne Central Business District (CBD); (3) evaluation of GNSS performance for every specified location in the urban canyons; and (4) visualisation and characterisation of the dynamic spatial patterns of GNSS performances in the urban canyons. As expected, the study shows that the integration of the GPS and Galileo constellations results in higher availability and stronger geometry, leading to significant improvement of GNSS performance in urban canyons of Melbourne CBD. Some conclusions are drawn and further research currently undertaken is also outlined.

Network-based Real Time Kinematic (NRTK) GPS positioning is considered to be a superior solution compared to the conventional single reference station based Real Time Kinematic (RTK) GPS positioning technique whose accuracy is highly affected by the distance dependent errors such as satellite orbital and atmospheric biases. NRTK GPS positioning uses raw measurements gathered from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance dependent errors within the area covered by the CORS. This technique has been developed and tested considerably during recent years and the overall performance in terms of achievable accuracies, reliability and mobility is as good as or even better than can be achieved using the conventional RTK GPS positioning technique. Currently, there are several commercial NRTK services around the world. In the United Kingdom (UK), for instance, Leica Geosystems in partnership with Ordnance Survey has been offering a NRTK GPS service since 2006. This service is called SmartNet and it can provide continuous centimetric level of accuracy to its subscribers. However, NRTK GPS positioning is particularly constrained by wireless data link coverage, correction transmission delay and completeness, GPS signal availability, etc., which could downgrade the positioning quality of the NRTK results. The paper presents some preliminary testing results of an investigation of the SmartNet service from the end users' point of view. A snapshot of the service's performance was carried out as part of a recent PhD studentship jointly awarded by the UK's Engineering and Physical Sciences Research Council (EPSRC) and Leica Geosystems (UK) to conduct comprehensive research into NRTK GPS quality control measures at the Institute of Engineering Surveying and Space Geodesy (IESSG), the University of Nottingham. In order to evaluate the service's quality several static and kinematic tests were performed using the same type of equipment and in the same way that the SmartNet subscribers would have used it. Centimetric accuracy was generally attained during both static and kinematic tests. This high accuracy was only affected by some level of unavailability mainly caused by GPS signal blockage. Additionally, the influence of the number of satellites in view, dilution of precision (DOP) and age of corrections (AoC) over the accuracy and stability of the NRTK GPS solution was also investigated during this research and presented in the paper.

Multipath effects on pseudorange measurements even after antenna design improvements and signal processing are still significant, and can't be ignored in many cases. Data processing techniques to mitigate multipath effects, Least Mean Square (LMS) filters specifically, are investigated in this paper. Since the multipath effects repeat every sidereal day, if the receiver environment is unchanged, the strong correlation between two successive days' pseudorange observations, which is assumed to be mostly due to multipath effects, can be extracted by an LMS filter. In addition, the frequency shifting characteristics of the two successive days' correlation vector demands an adaptive filter. In this paper the performance of two LMS adaptive filters, the standard LMS adaptive filter and the variable length LMS (VLLMS) adaptive filter, are investigated. This paper reports the results from an analysis of observation data from SydNET CORS receivers.

The Advanced Land Observing Satellite (ALOS), launched on 24 January 2006, is a Japanese satellite carrying an L-band SAR sensor, namely the PALSAR, which is expected to demonstrate good performance in applications such as crustal deformation measurement, subsidence detection and landslide monitoring. This paper describes a case study of Differential Synthetic Aperture Radar Interferometry (DInSAR) using ALOS/PALSAR data to detect crustal deformation caused by a recent small earthquake in Western Australia. Single Look Complex (SLC) images acquired by the ALOS/PALSAR sensor were used to measure the co-seismic deformation of the 9 October 2007 earthquake that occurred south of the town of Katanning, in the state of Western Australia. Three images with dual polarizations (HH & HV) were used in this study; two acquired before the earthquake event and one after. The two-pass DInSAR processing method was applied to generate differential interferograms. The peak-to-peak surface displacement is up to 32 centimetres in the radar line-of-sight direction. The interferograms were used to constrain the fault modelling. The co-seismic displacements were modelled using a two-segment uniform slip model in a homogeneous isotropic half-space. A genetic algorithm was used to determine the optimal source parameters for the nonlinear inversion. The resultant maximum slip is about 0.4 metres on an almost pure reverse fault striking ~NE55° and dipping at ~40°S. The scalar moment was estimated to be 1:84 × 10 16 Nm ( Mw 4.8), which is in good agreement with the seismological results. The root-mean-square difference between the DInSAR observed and modelled displacements is 1.6 cm.

The factors which control the crest settlements of the Kremasta Dam (Central Greece), one of the highest earthfill dams in Europe, were analyzed on the basis of the threshold correlation. The latter is a new stochastic technique which permits identification of parts of time series which are highly correlated on the basis of the optimization of the correlation coefficient between two parameters in combination with a high-pass filter of gradually increasing width. Based on a unique, >35 years long monitoring record consisting of leveling data, reservoir levels and precipitation levels we formed three time series describing the parameters which may affect the dam geometry (fluctuations of the reservoir level, of the rate of change of the reservoir level and of the rate of rainfall) and a fourth time series describing the dam settlements (settlement index fluctuations). Threshold correlation analysis permitted to identify critical levels for each of the above three parameters influencing the dam settlements. It was found that none of the three parameters alone can explain excessive dam settlements, but if the critical values of the three parameters are at the same time exceeded (i.e. under a conditional Boolean probability), the rate of the settlements exceeds its critical value. This approach may prove useful for other dams, but also in the analysis of data in other fields of science and engineering.