Volume 1, Issue 2

Textbooks on geodetic surveying usually describe a very small number of principles which should provide the foundation of geodetic surveying. Here, the author argues that an applied field, such as engineering geodesy, has a methodology as foundation rather than a few principles. Ten methodological elements (ME) are identified: (1) Point discretisation of natural surfaces and objects, (2) distinction between coordinate and observation domain, (3) definition of reference systems, (4) specification of unknown parameters and desired precisions, (5) geodetic network and observation design, (6) quality control of equipment, (7) quality control of measurements, (8) establishment of measurement models, (9) establishment of parameter estimation models, (10) quality control of results. Each ME consists of a suite of theoretical developments, geodetic techniques and calculation procedures, which will be discussed. This paper is to be considered a first attempt at identifying the specific elements of the methodology of engineering geodesy. A better understanding of this methodology could lead to an increased objectivity, to a transformation of subjective practical experiences into objective working methods, and consequently to a new structure for teaching this rather diverse subject.

In structural deformation analysis the behaviour of the monitored structure is typically described in a dynamic model, by deducing a weighting or transfer function. Its parameters can be estimated from the recorded influencing and deformation signals, using system theory and time series or regression analysis. The analysing functions or the adjustment models can be determined using the entire data set, if the assumption of stationarity up to the 2 nd order is fulfilled. This is the case if the monitoring activity extends over a long time and the influences on the structure maintain their statistical properties from a long-term point of view. However due to the higher recording rates made possible by some modern sensors also short-term deformations and influencing factors like wind or traffic, which expose a more irregular pattern, can be registered and included in the investigation of the structures' behaviour. In these cases the stationarity assumption needs a more careful analysis. The data segments with homogeneous statistical properties have to be identified and different model parameters have to be estimated for each of them. This paper deals with an approach for the detection of the change-points in the statistical properties of the data. The method is based on the likelihood function. In this approach the change-points are estimated by minimising a penalised contrast function. To get a better insight in the behaviour of the structure when several influencing factors overlap, the signals are first decomposed using the undecimated Wavelet Transform. The change-point method is applied to the obtained signal components. The performance of the approach is assessed by analysing simulated and real data, recorded during the monitoring of a wind energy turbine and a vertical lift bridge.

Since the beginning of June 1999 the Institute of Geodesy and Photogrammetry is monitoring the displacements of a small, but unstable bridge within the city of Brunswick. As basic sensor for automatic and continuous monitoring an automated total station is installed below the bridge, which takes measurements to 180 reflecting targets on the structure three times per day. This paper begins with the outline of the basic concepts for the instrument setup, the longterm control of its stability and possible influencing factors. Then the processing of the time series of raw data is discussed, emphasing the filtering concepts to account for the detection of outliers, due to target corrosion or distortion. Finally the bridge is considered as a closed system and all the available information is used to generate a non-parametric model to describe its behaviour with tools from system analysis.

Safety of earth dams depends on the proper design, construction, and monitoring of actual behaviour during the construction and during the operation of the structure. The most critical factor in the assessment of the safety threshold value of any structure is the acceleration of its deformation. Therefore, the designed accuracy of monitoring surveys must fulfill requirements of detecting accelerations at critical locations of the investigated object. As an example, time dependant behavior of a large embankment dam during filling up the reservoir has been analyzed and verified by comparing monitoring results with the deterministic (prediction) model of the deformation. The geotechnical and geodetic monitoring besides providing a warning system in case of an abnormal behaviour of the dam, may be used as a tool for a verification of design parameters where geotechnical parameters are of the highest importance. Modeling of deformation of earth dams is a complex process in which one should consider the nonlinear behaviour of the construction material, interaction between the structure and the underlying foundation strata, influence of water load on the structure and on the foundation bedrock, and the effects of water saturation. Due to the uncertainty of the model parameters, careful monitoring of the dam and its surroundings are required in order to verify and enhance the model. In addition, with properly designed monitoring surveys, one may also determine the actual deformation mechanism. The finite element method may be useful tool in the proper design of the monitoring scheme by providing information on the locations and magnitude of the expected maximum displacements and velocites of movements. The discussed problems are illustrated by three types of earth dams located in California, U.S.A. and in Quebec, Canada.

Most of the present techniques for integrating Inertial Measurement Units (IMU) and Global Positioning Systems (GPS) utilize Kalman filtering (KF) as the integration estimation technique. KF is a recursive algorithm designed to compute corrections to a system based on external measurements. In inertial navigation, this can be accomplished by using an external navigation reference such as GPS. As long as GPS measurements are available, the KF solution of IMU/GPS integration works efficiently and provides accurate estimate of the navigation states. Nevertheless, during GPS signal outages, the functionality of KF update engine is disrupted due to the lack of GPS update measurements and therefore KF works only in prediction mode. Moreover, IMUs, particularly those integrating low-cost sensors, suffer from one serious limitation: drift rate errors rapidly accumulate with the passage of time. As a result, the corresponding state estimate will also quickly drift over time causing a dramatic degradation in the overall accuracy of the integrated system. Performance improvements of integrated IMUs, utilizing low-cost sensors, and GPS are presented in this paper. This achieved through the implantation of a new technique which augment KF and Fuzzy logic principles. In the innovation in the new technique is in its ability to generate the update measurements (positions and velocity error measurements) to the KF update engine even during GPS signal outages. This proposed technique has been tested on real MEMS inertial and GPS data collected in a land vehicle navigation test. The test results indicate that the proposed Fuzzy model can efficiently compensate for GPS updates during short GPS signal outages.

Location based-services and personal navigation require the location determination of a user not only in outdoor environments but also indoor. For indoor location already established wireless infrastructure such as WiFi (Wireless Fidelity) or WLAN (Wireless Local Area Networks) can be employed. This approach has the advantage that no costly hardware installations are necessary inside a building if WiFi is already available. The IMST GmbH has developed a software framework called "ipos" for indoor positioning technologies. The principle of the developed platform and the performance of the location determination using WiFi in a localization testbed of IMST GmbH are presented in this paper.