Quantifying deformation in North Borneo with GPS

The existence of intra-plate deformation of the Sundaland platelet along its eastern edge in North Borneo, South-East Asia, makes it an interesting area that still is relatively understudied. In addition, the motion of the coastal area of North-West Borneo is directed toward a frontal fold-and-thrust belt and has been fueling a long debate on the possible geophysical sources behind it. At present this fold-and-thrust belt is not generating significant seismic activity and may also not be entirely active due to a decreasing shelfal extension from south to north. Two sets of Global Positioning System (GPS) data have been used in this study; the first covering a time period from 1999 until 2004 (ending just before the Giant Sumatra–Andaman earthquake) to determine the continuous Sundaland tectonic plate motion, and the second from 2009 until 2011 to investigate the current deformations of North Borneo. Both absolute and relative positioning methods were carried out to investigate horizontal and vertical displacements. Analysis of the GPS results indicates a clear trend of extension along coastal regions of Sarawak and Brunei in North Borneo. On the contrary strain rate tensors in Sabah reveal that only insignificant and inconsistent extension and compression occurs throughout North-West Borneo. Moreover, station velocities and rotation rate tensors on the northern part of North Borneo suggest a clockwise (micro-block) rotation. The first analysis of vertical displacements recorded by GPS in North-West Borneo points to low subsidence rates along the western coastal regions of Sabah and inconsistent trends between the Crocker and Trusmadi mountain ranges. These results have not been able to either confirm or reject the hypothesis that gravity sliding is the main driving force behind the local motions in North Borneo. The ongoing Sundaland–Philippine Sea plate convergence may also still play an active role in the present-day deformation (crustal shortening) in North Borneo and the possible clockwise rotation of the northern part of North Borneo as a micro-block. However, more observations need to be collected to determine if the northern part of North Borneo indeed is (slowly) moving independently

Wavelet packets based denoising method for measurement domain repeat-time multipath filtering in GPS static high-precision positioning

Repeatable satellite orbits can be used for multipath mitigation in GPS-based deformation monitoring and other high-precision GPS applications that involve continuous observation with static antennas. Multipath signals at a static station repeat when the GPS constellation repeats given the same site environment. Repeat-time multipath filtering techniques need noise reduction methods to remove the white noise in carrier phase measurement residuals in order to retrieve the carrier phase multipath corrections for the next day. We propose a generic and robust three-level wavelet packets based denoising method for repeat-time-based carrier phase multipath filtering in relative positioning; the method does not need tuning to work with different data sets. The proposed denoising method is tested rigorously and compared with two other denoising methods. Three rooftop data sets collected at the University of Nottingham Ningbo China and two data sets collected at three Southern California Integrated GPS Network high-rate stations are used in the performance assessment. Test results of the wavelet packets denoising method are compared with the results of the resistor–capacitor (RC) low-pass filter and the single-level discrete wavelet transform (DWT) denoising method. Multipath mitigation efficiency in carrier phase measurement domain is shown by spectrum analysis of two selected satellites in two data sets. The positioning performance of the repeat-time-based multipath filtering techniques is assessed. The results show that the performance of the three noise reduction techniques is about 1–46 % improvement on positioning accuracy when compared with no multipath filtering. The statistical results show that the wavelet packets based denoising method is always better than the RC filter by 2–4 %, and better than the DWT method by 6–15 %. These results suggest that the proposed wavelet packets based denoising method is better than both the DWT method and the relatively simple RC low-pass filter for noise reduction in multipath filtering. However, the wavelet packets based denoising method is not significantly better than the RC filter

5th International Symposium on Satellite Navigation Technology and Applications

The classical least-squares processing of GPS measurements generates residuals, which contains the signature of both unmodelled systematic biases and random measurement noise. It is desirable to extract (or minimise) the systematic biases contained within the GPS measurements. This would be relatively straightforward if there were some apriori knowledge of the phenomena related to these errors. Common ways of dealing with this problem include (i) changes to the stochastic modelling, and (ii) redefinition of the functional model. In this study, we apply a method based on wavelets to decompose GPS double-differenced residuals into a low-frequency bias term and a high-frequency noise term. The extracted bias component is then applied directly to the GPS measurements to correct for this term. The remaining terms, largely characterised by the GPS range measurements and high-frequency measurement noise, are expected to give the best linear unbiased solutions from a leastsquares process. A robust VCV estimation, using the MINQUE procedure, controls the formulation of the stochastic model. The results show that this method can improve both the ambiguity resolution and the accuracy of the estimated baseline component

Modelling residual systematic errors in GPS

Since its introduction to civilian users in the early 1980's, the Global Positioning System (GPS) has been playing an increasingly important role in high precision surveying and geodetic applications. Like traditional geodetic network adjustment, data processing for precise GPS static positioning is invariably performed using the least squares method. To employ the least squares method for GPS relative positioning, both the functional and stochastic models of the GPS measurements need to be defined. The functional model describes the mathematical relationship between the GPS observations and the unknown parameters, while the stochastic model describes the statistical characteristics of the GPS observations. The stochastic model is therefore dependent on the choice of the functional model. A doubledifferencing technique is commonly used for constructing the functional model. In current stochastic models, it is usually assumed that all the one-way measurements have equal variance, and that they are statistically independent. The above functional and stochastic models have therefore been used in standard GPS data processing algorithms