Reliable estimation of orbit errors in spaceborne SAR interferometry. The network approach

An approach to improve orbital state vectors by orbit error estimates derived from residual phase patterns in synthetic aperture radar interferograms is presented. For individual interferograms, an error representation by two parameters is motivated: the baseline error in cross-range and the rate of change of the baseline error in range. For their estimation, two alternatives are proposed: a least squares approach that requires prior unwrapping and a less reliable gridsearch method handling the wrapped phase. In both cases, reliability is enhanced by mutual control of error estimates in an overdetermined network of linearly dependent interferometric combinations of images. Thus, systematic biases, e.g., due to unwrapping errors, can be detected and iteratively eliminated. Regularising the solution by a minimum-norm condition results in quasi-absolute orbit errors that refer to particular images. For the 31 images of a sample ENVISAT dataset, orbit corrections with a mutual consistency on the millimetre level have been inferred from 163 interferograms. The method itself qualifies by reliability and rigorous geometric modelling of the orbital error signal but does not consider interfering large scale deformation effects. However, a separation may be feasible in a combined processing with persistent scatterer approaches or by temporal filtering of the estimates

Network Adjustment of Orbit Errors in SAR Interferometry

Orbit errors can induce significant long wavelength error signals in synthetic aperture radar (SAR) interferograms and thus bias estimates of wide-scale deformation phenomena. The presented approach aims for correcting orbit errors in a preprocessing step to deformation analysis by modifying state vectors. Whereas absolute errors in the orbital trajectory are negligible, the influence of relative errors (baseline errors) is parametrised by their parallel and perpendicular component as a linear function of time. As the sensitivity of the interferometric phase is only significant with respect to the perpendicular baseline and the rate of change of the parallel baseline, the algorithm focuses on estimating updates to these two parameters. This is achieved by a least squares approach, where the unwrapped residual interferometric phase is observed and atmospheric contributions are considered to be stochastic with constant mean. To enhance reliability, baseline errors are adjusted in an overdetermined network of interferograms, yielding individual orbit corrections per acquisition

Extension of an automatic building extraction technique to airborne laser scanner data containing damaged buildings

Airborne laser scanning systems generate 3-dimensional point clouds of high density and irregular spacing. These data consist of multiple returns coming from terrain, buildings, and vegetation. The major difficulty is the extraction of object categories, usually buildings. In the field of disaster management, the detection of building damages plays an important role. Therefore, the question arises, if damaged buildings can also be detected by a method developed for the automatic extraction of buildings. Another purpose of this study is to extend and test an automatic building detection method developed initially for first echo laser scanner data on data captured in first and last echo. In order to answer these two questions, two institutes share their data and knowledge: the Institute of Photogrammetry and Remote Sensing (IPF, Universität Karlsruhe (TH), Germany) and the MAP-PAGE team (INSA de Strasbourg, France). The used 3D LIDAR data was captured over an area containing undamaged and damaged buildings. The results achieved for every single processing step by applying the original and the extended algorithm to the data are presented, analysed and compared. It is pointed out which buildings can be extracted by which algorithm and why some buildings remain undetecte

A Monte-Carlo generator for statistical hadronization in high energy e+e- collisions

We present a Monte-Carlo implementation of the Statistical Hadronization Model in e+e- collisions. The physical scheme is based on the statistical hadronization of massive clusters produced by the event generator Herwig within the microcanonical ensemble. We present a preliminary comparison of several observables with measurements in e+e- collisions at the Z peak. Although a fine tuning of the model parameters is not carried out, a general good agreement between its predictions and data is found.Comment: 19 pages, 28 figures, 6 tables. v2: added sections on comparison between the Statistical Hadronization Model and the Cluster Model and on the interplay between Herwig cluster splitting algorithm and Statistical Hadronization Model predictions. Fixed typos and references added. Version accepted for publication in EPJ

The H1 Forward Proton Spectrometer at HERA

The forward proton spectrometer is part of the H1 detector at the HERA collider. Protons with energies above 500 GeV and polar angles below 1 mrad can be detected by this spectrometer. The main detector components are scintillating fiber detectors read out by position-sensitive photo-multipliers. These detectors are housed in so-called Roman Pots which allow them to be moved close to the circulating proton beam. Four Roman Pot stations are located at distances between 60 m and 90 m from the interaction point.Comment: 20 pages, 10 figures, submitted to Nucl.Instr.and Method

Prototype design of a timing and fast control system in the CBM experiment

The Compressed Baryonic Matter (CBM) experiment is designed to handle interaction rates of up to 10 MHz and up to 1 TB/s of raw data generated. With triggerless streaming data acquisition in the experiment and beam intensity fluctuations, it is expected that occasional data bursts will surpass bandwidth capabilities of the Data Acquisition System (DAQ) system. In order to preserve integrity of event data, the bandwidth of DAQ must be throttled in an organised way with minimum information loss. The Timing and Fast Control (TFC) system provides a latency-optimised datapath for throttling commands and distributes a system clock together with a global timestamp. This paper describes a prototype design of the system with focus on synchronisation and its evaluation

Prototype design of a timing and fast control system in the CBM experiment

The Compressed Baryonic Matter (CBM) experiment is designed to handle interaction rates of up to 10 MHz and up to 1 TB/s of raw data generated. With triggerless streaming data acquisition in the experiment and beam intensity fluctuations, it is expected that occasional data bursts will surpass bandwidth capabilities of the Data Acquisition System (DAQ) system. In order to preserve integrity of event data, the bandwidth of DAQ must be throttled in an organised way with minimum information loss. The Timing and Fast Control (TFC) system provides a latency-optimised datapath for throttling commands and distributes a system clock together with a global timestamp. This paper describes a prototype design of the system with focus on synchronisation and its evaluation

On the Effect of Reference Frame Motion on InSAR Deformation Estimates

For processing of interferometric synthetic aperture radar (InSAR) data, precise satellite orbits are required. These orbits are given in a reference frame with respect to which tectonic plates perform a relative motion. Neglecting this motion can cause temporally increasing baseline errors that induce large scale error ramps into the interferometric phase. The amount of error depends on the geographical location and is evaluated globally for the ENVISAT orbit. Predicted biases of deformation estimates can reach up to 7 mm/a in some areas. Whereas these biases are not separable from actual deformation signals by spatio-temporal correlation properties, they are well predictable and can easily be accounted for. A most simple correction approach consists in compensating the plate motion by modifying orbital state vectors, assuming a homogeneous velocity for the whole plate. This approach has been tested on Persistent Scatterer Interferometry (PSI) results over the area of Groningen, the Netherlands

A 3D track finder for the Belle II CDC L1 trigger

Machine learning methods are integrated into the pipelined first level (L1) track trigger of the upgraded flavor physics experiment Belle II at KEK in Tsukuba, Japan. The novel triggering techniques cope with the severe background from events outside the small collision region provided by the new SuperKEKB asymmetric-energy electron-positron collider. Using the precise drift-time information of the central drift chamber which provides axial and stereo wire layers, a neural network L1 trigger estimates the 3D track parameters of tracks, based on input from the axial wire planes provided by a 2D track finder. An extension of this 2D Hough track finder to a 3D finder is proposed, where the single hit representations in the Hough plane are trained using Monte Carlo. This 3D finder improves the track finding efficiency by including the stereo sense wires as input. The estimated polar track angle allows a specialization of the subsequent neural networks to sectors in the polar angle