Frank Neitzel

Particle filter-based estimation of inter-system phase bias for real-time integer ambiguity resolution

Although double-differenced (DD) observations between satellites from different systems can be used in multi-GNSS relative positioning, the inter-system DD ambiguities cannot be fixed to integer because of the existence of the inter-system bias (ISB). Obviously, they can also be fixed as integer along with intra-system DD ambiguities if the associated ISBs are well known. It is critical to fix such inter-system DD ambiguities especially when only a few satellites of each system are observed. In most of the existing approaches, the ISB is derived from the fractional part of the inter-system ambiguities after the intra-system DD ambiguities are successfully fixed. In this case, it usually needs observations over long times depending on the number of observed satellites from each system. We present a new method by means of particle filter to estimate ISBs in real time without any a priori information based on the fact that the accuracy of a given ISB value can be qualified by the related fixing RATIO. In this particle filter-based method, the ISB parameter is represented by a set of samples, i.e., particles, and the weight of each sample is determined by the designed likelihood function related to the corresponding RATIO, so that the true bias value can be estimated successfully. Experimental validations with the IGS multi-GNSS experiment data show that this method can be carried out epoch by epoch to provide precise ISB in real time. Although there are only one, two, or at most three Galileo satellites observed, the successfully fixing rate increases from 75.5% for GPS only to 81.2%. In the experiment with five GPS satellites and one Galileo satellites, the first successfully fixing time is reduced to half of that without fixing the inter-system DD ambiguities.

Testing the compatibility of constraints for parameters of a geodetic adjustment model

Geodetic adjustment models are often set up in a way that the model parameters need to fulfil certain constraints. The normalized Lagrange multipliers have been used as a measure of the strength of constraint in such a way that if one of them exceeds in magnitude a certain threshold then the corresponding constraint is likely to be incompatible with the observations and the rest of the constraints. We show that these and similar measures can be deduced as test statistics of a likelihood ratio test of the statistical hypothesis that some constraints are incompatible in the same sense. This has been done before only for special constraints (Teunissen in Optimization and Design of Geodetic Networks, pp. 526–547, 1985). We start from the simplest case, that the full set of constraints is to be tested, and arrive at the advanced case, that each constraint is to be tested individually. Every test is worked out both for a known as well as for an unknown prior variance factor. The corresponding distributions under null and alternative hypotheses are derived. The theory is illustrated by the example of a double levelled line.

Determination of Intensity-Based Stochastic Models for Terrestrial Laser Scanners Utilising 3D-Point Clouds

Recent advances in stochastic modelling of reflectorless rangefinders revealed an inherent relationship among raw intensity values and the corresponding precision of observed distances. In order to derive the stochastic properties of a terrestrial laser scanner’s (TLS) rangefinder, distances have to be observed repeatedly. For this, the TLS of interest has to be operated in the so-called 1D-mode—a functionality which is offered only by a few manufacturers due to laser safety regulations. The article at hand proposes two methodologies to compute intensity-based stochastic models based on capturing geometric primitives in form of planar shapes utilising 3D-point clouds. At first the procedures are applied to a phase-based Zoller + Fröhlich IMAGER 5006h. The generated results are then evaluated by comparing the outcome to the parameters of a stochastic model which has been derived by means of measurements captured in 1D-mode. Another open research question is if intensity-based stochastic models are applicable for other rangefinder types. Therefore, one of the suggested procedures is applied to a Riegl VZ-400i impulse scanner, as well as a Leica ScanStation P40 TLS that deploys a hybrid rangefinder technology. The generated results successfully demonstrate alternative methods for the computation of intensity-based stochastic models as well as their transferability to other rangefinder technologies.

Lagrange-Multiplikatoren (LM) der Ausgleichungsrechnung als Indikator für Strukturschäden

Zusammenfassung Die ständige Überwachung von Bauwerken gewinnt zunehmend an Bedeutung. Mit Blick auf die Nachhaltigkeit ist die Verlängerung der Nutzungsdauer bestehender baulicher Konstruktionen von unschätzbarem Wert, sowohl aus finanziellen Gründen als auch unter Denkmalaspekten. Eine neue Methode zur Detektion von Strukturschädigungen basiert auf einer integrierten Auswertung von Messwerten verschiedener Sensoren nach der Methode der kleinsten Quadrate sowie der Interpretation der dabei auftretenden Langrange-Multiplikatoren. Diese Methode der Schädigungsanalyse wird anhand eines numerischen Beispiels aus einem Vier-Punkt-Biegeversuch mit einem Verbundträger näher erläutert. Abstract Structural health monitoring of structures is gaining increasingly importance. With regard to sustainability it is of great value for both financial reasons (to extend the useful life of existing architectural structures) and the aspects of listed buildings. A new method for the detection of structural damage is based on an integrated analysis of measurements of different sensors according to the method of least squares and the interpretation of the occurring Lagrange Multipliers. This method of damage analysis is illustrated by a numerical example of a four-point bending test with a composite beam.

Determining inter-system bias of GNSS signals with narrowly spaced frequencies for GNSS positioning

Relative positioning using multi-GNSS (global navigation satellite systems) can improve accuracy, reliability, and availability compared to the use of a single constellation system. Intra-system double-difference (DD) ambiguities (ISDDAs) refer to the DD ambiguities between satellites of a single constellation system and can be fixed to an integer to derive the precise fixed solution. Inter-system ambiguities, which denote the DD ambiguities between different constellation systems, can also be fixed to integers on overlapping frequencies, once the inter-system bias (ISB) is removed. Compared with fixing ISDDAs, fixing both integer intra- and inter-system DD ambiguities (IIDDAs) means an increase of positioning precision through an integration of multiple GNSS constellations. Previously, researchers have studied IIDDA fixing with systems of the same frequencies, but not with systems of different frequencies. Integer IIDDAs can be determined from single-difference (SD) ambiguities, even if the frequencies of multi-GNSS signals used in the positioning are different. In this study, we investigated IIDDA fixing for multi-GNSS signals of narrowly spaced frequencies. First, the inter-system DD models of multi-GNSS signals of different frequencies are introduced, and the strategy for compensating for ISB is presented. The ISB is decomposed into three parts: 1) a float approximate ISB number that can be considered equal to the ISB of code pseudorange observations and thus can be estimated through single point positioning (SPP); 2) a number that is a multiple of the GNSS signal wavelength; and 3) a fractional ISB part, with a magnitude smaller than a single wavelength. Then, the relationship between intra- and inter-system DD ambiguity RATIO values and ISB was investigated by integrating GPS L1 and GLONASS L1 signals. In our numerical analyses with short baselines, the ISB parameter and IIDDA were successfully fixed, even if the number of observed satellites in each system was small.

Improvements on the particle-filter-based GLONASS phase inter-frequency bias estimation approach

The carrier phase inter-frequency bias (IFB) of GLONASS between receivers of different types is usually not zero. This bias must be estimated and removed in data processing so that the integer double difference (DD) ambiguities can be fixed successfully. Recently, the particle filter approach has been proposed to estimate the IFB rate in real time. In this approach, the IFB rate samples are first generated and used to correct the phase IFB in the GLONASS observations. Then, the weights of the rate samples are updated with a function related to RATIO which is for ambiguity acceptance testing in integer ambiguity resolution. Afterwards, the IFB rate is estimated according to the weighted particles. This approach can estimate IFB accurately with short convergence time and without prior information. However, when the system noise is set too low, the estimated results are unstable due to the serious problem of particle diversity-loss, even though the system model is accurate. Additionally, the computational burden is dependent on the number of particles, which has to be optimized for the computation at hand. Therefore, this study proposes two improvements for the IFB estimation in regard to the above two aspects. The first improvement is to solve the noise setting problem by employing a regularized particle filter (RPF). The second improvement optimizes the number of particles in the resampling step according to the standard deviation (STD) of the weighted particles via a controlling function. The two improvements result in significantly better performances. The regularization method allows for the system noise to be set as zero without disturbing the estimates, and consequently, more precise estimates can be achieved. In addition, the approach using the controlling function for adapting the number of particles has comparable performance in precision but the computation load is largely reduced.

Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring

Current research questions in the field of geomorphology focus on the impact of climate change on several processes causing subsequently natural hazards. Geodetic deformation measurements are a suitable tool to document such 10 geomorphic mechanisms e.g. under the use ofby capturing a region of interest with terrestrial laser scanners that capture a region of interest in a quasi-laminar fashion which results in a so-called 3D point cloud. In this context the centralThe main problem is linked toin deformation monitoring is the transformation of 3D point clouds captured at different points in time (epochs) into a commonstable reference coordinate system. To date, this step has been mostly carried out by usage of artificial targets and/or controlpoints with known coordinates, so called reference points. Several drawbacks are related to 15 this strategy such as the enormous effort to distribute the targets in object spacewithin the area of interest, the required survey by additional geodetic sensors such as total stations or GNSS-receivers as well as the limited extent within the region of interest. In this contribution a surface-based registration methodology is presented, termed the iterative closest proximity algorithm (ICProx), that solely uses pointspoint cloud data as input, similar to the iterative closest point-algorithm (ICP), and hence does not require any artificial targets or extracted geometric primitives, such as planes. The aim of this study was to 20 automatically determineclassify deformations that occurred at a rock glacier, an ice glacier as well as in a rock fall area. For every example two epochs were processed while the ICProx-algorithm’s classification accuracy is 70% on average in comparison to reference data.

Advances in Structural Monitoring by an Integrated Analysis of Sensor Measurements and 3D Building Model

The use of open GIS standards offers a broad variety of potential, particularly in the field of data exchange, data storage, and interoperability. GML and CityGML are excellent examples for the ontological description of real world objects by means of an open standard whereas SensorML serves to describe measurements, sensors and measuring platforms. The use of such standards offers not only the possibility of using a common standardised language, but also the use of open service standards. The combination of spatial data and sensor standards in services and service-oriented architectures goes far beyond previous existing solutions on the market and provides a novel platform for monitoring structures. That in fact is far more than a simple data storage model. The methods and models presented in this contribution allow a direct integration of sensor data and its provision through an open standard language. In this case, all the intermediate steps at any time through an open service interface are addressed and may be made available and provided to different actors and stakeholders participating in a construction scenario. The great potential and the added value of such an information system is the permanent availability of measurement and object data and an associated integrated analysis of sensor data in combination with a finite element model (FEM). The automatic derivation of a finite element model from the 3D structure model, the visualisation of FEM, the provision of raw (measurement) data and sensor information for each time of measurement transform the platform into a universal tool in the field of structural monitoring. This contribution introduces the individual components, the standards used and the interaction between the components to an overall system.