Andreas Baumann-Ouyang

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Baumann-Ouyang

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Andreas

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0000-0002-8306-3642

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Publications 1 - 8 of 8

MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring
Baumann-Ouyang, Andreas; Butt, Jemil Avers; Varga, Matej; et al. (2023)
Energies
Deformations affect the structural integrity of wind turbine towers. The health of such structures is thus assessed by monitoring. The majority of sensors used for this purpose are costly and require in situ installations. We investigated whether Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO-SAR) sensors can be used to monitor wind turbine towers. We used an automotive-grade, low-cost, off-the-shelf MIMO-SAR sensor operating in the W-band with an acquisition frequency of 100 (Formula presented.) to derive Line-Of-Sight (LOS) deformation measurements in ranges up to about 175 (Formula presented.). Time series of displacement measurements for areas at different heights of the tower were analyzed and compared to reference measurements acquired by processing video camera recordings and total station measurements. The results showed movements in the range of up to 1 (Formula presented.) at the top of the tower. We were able to detect the deformations also with the W-band MIMO-SAR sensor; for areas with sufficient radar backscattering, the results suggest a sub-mm noise level of the radar measurements and agreement with the reference measurements at the mm- to sub-mm level. We further applied Fourier transformation to detect the dominant vibration frequencies and identified values ranging from 0.17 to 24 (Formula presented.). The outcomes confirmed the potential of MIMO-SAR sensors for highly precise, cost-efficient, and time-efficient structural monitoring of wind turbine towers. The sensors are likely also applicable for monitoring other high-rise structures such as skyscrapers or chimneys.
Automatic calibration of terrestrial laser scanners using intensity features
Qiao, Jing; Medic, Tomislav; Baumann-Ouyang, Andreas (2023)
The photogrammetric record
We propose an in situ self-calibration method by detecting and matching intensity features on the local planes in overlapping point clouds based on the Förstner operator. We successfully matched the intensity features from scans at different locations by feature matching on common local planes rather than on the rasterised grids of the horizontal and vertical angles adopted by the affirmed keypoint-based algorithm. The capability of extracting features from different stations offers the possibility of comprehensive scanner calibration, solving the disadvantage that the existing keypoint-based methods can only estimate the two-face-sensitive model parameters. The proposed algorithm has been tested with a high-precision panoramic scanner, Leica RTC360, using datasets from a calibration hall and a general working scenario. It has been shown that the proposed approach consistently calibrates the two-face-sensitive model parameters with the affirmed keypoint-based one. For the case of comprehensive calibration with the offset estimated and some angular parameters separated where the previous keypoint-based one failed, the proposed algorithm achieves an accuracy of 0.16 mm, 2.7″ and 2.1″ in range, azimuth and elevation for the estimated target centres. The proposed algorithm can accurately calibrate two-face-sensitive and more comprehensive model parameters without any preparation on-site, for example, mounting targets.
Automatic extrinsic calibration of terrestrial laser scanner and digital camera by MoG image correlation
Qiao, Jing; Wu, Hangbin; Baumann-Ouyang, Andreas; et al. (2023)
International Journal of Applied Earth Observation and Geoinformation
The current terrestrial laser scanners (TLS) are generally equipped with digital cameras which can capture the scene along with the scanner. These two types of sensors offer complementary properties in modeling and visualization of real-world scenes. TLSs can provide geometric information of the real scene with accurate 3D coordinates of the point clouds; cameras are used to acquire high-resolution images and provide good texture information of the environment. Fusing the extracted information from these two sensors helps to create a better virtual representation of the real-world. For a TLS with several external cameras, their acquisition centers are not identical and the axis of their coordinate systems are not aligned either. This paper proposes an automatic camera and TLS extrinsic calibration approach using correspondences extracted from both measurements. To overcome the intrinsic difference between back-projected images of point clouds colored by intensities and the RGB camera images, we innovatively generate both magnitude of gradient images, enabling effective image correlation and accurate correspondence extraction. The 3 external cameras mounted on top, side and bottom of Leica RTC360 3D laser scanner are calibrated. Dependent on the distribution of observations, we achieve different calibration accuracy for each camera. With scans from multiple stations, the cameras obtain an offset accuracy of 0.12 – 0.36 mm and angular accuracy of 3.7 – 8.3″. After calibration, the excellent overlap of images from the two sensors further verifies the proposed method's success. The idea of correspondence identification demonstrated in this study can also be applied to the extrinsic calibration/registration of other types of scanner and digital cameras.
Estimating 3D displacement vectors from line-of-sight observations with application to MIMO-SAR
Baumann-Ouyang, Andreas; Butt, Jemil Avers; Wieser, Andreas (2023)
Journal of Applied Geodesy
Displacements in typical monitoring applications occur in 3D but having sensors capable of measuring such 3D deformations with areal coverage is rare. One way could be to combine three or more line-of-sight measurements carried out from different locations at the same time and derive 3D displacement vectors. Automotive Multiple-Input-Multiple-Output Synthetic Aperture Radar (MIMO-SAR) systems are of interest for such monitoring applications as they can acquire line-of-sight displacement measurements with areal coverage and are associated with low cost and high flexibility. In this paper, we present a set of algorithms deriving 3D displacement vectors from line-of-sight displacement measurements while applying spatial and temporal least squares adjustments. We evaluated the algorithms on simulated data and tested them on experimentally acquired MIMO-SAR acquisitions. The results showed that especially spatial parametric and non-parametric least squares adjustments worked very well for typical displacements occurring in geomonitoring and structural monitoring (e.g. tilting, bending, oscillating, etc.). The simulations were confirmed by an experiment, where a corner cube was moved step-wise. The results show that acquisitions of off-the-shelf automotive-grade MIMO-SAR systems can be combined to derive 3D displacement vectors with high accuracy.
Bridge deformations during train passage: monitoring multiple profiles using concurrently operating MIMO-SAR sensors
Baumann-Ouyang, Andreas; Butt, Jemil Avers; Wieser, Andreas (2023)
Proceedings of the 5th Joint International Symposium on Deformation Monitoring - JISDM 2022
Sensors capable of measuring surface deformations with areal coverage and high spatial and temporal resolution are beneficial for many monitoring applications. However, such sensors are typically expensive, or their configuration cannot be adapted flexibly by the user like in case of satellite-based systems. Automotive Multiple-Input-Multiple-Output Synthetic Aperture Radar (MIMO-SAR) systems are interesting potential alternatives associated with low cost and high flexibility. In this paper, we present an experimental investigation showing the capabilities of a particular off-the-shelf, automotive radar system for structural monitoring. We analyse the accuracy of the measured line-of-sight displacements, the spatial and temporal resolution, and the impact of simultaneous coverage of the same area by two sensors of the same type. Finally, we demonstrate the MIMO-SAR system in a real-world use case measuring deformations of a railway bridge in response to dynamic load by trains passing over it. We operated two MIMO-SAR sensors simultaneously, analyse and interpret the individual interferograms and combine the data to derive the temporal and spatial distribution of vertical displacements along selected profiles. The results show that off-the-shelf automotive-grade MIMO-SAR systems can be used to quantify sub-millimetre deformations of structures and derive high-resolution time series beneficial for structural health monitoring applications.
Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety
Meng, Lingxuan; Peng, Zhixing; Zhou, Ji; et al. (2020)
Remote Sensing
Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.
MIMO-SAR Interferometric Measurements for Structural Monitoring: Accuracy and Limitations
Baumann-Ouyang, Andreas; Butt, Jemil A.; Salido Monzú, David; et al. (2021)
Remote Sensing
Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180◦ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25 µm can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.
Experimental Investigation of the Applicability of W-Band MIMO-SAR for Structural Health Monitoring
Baumann-Ouyang, Andreas (2023)
Due to constant exposure to environmental conditions and external forces, engineering structures like bridges, high-rise buildings, and others deteriorate over time. Structural Health Monitoring (SHM) aims to identify and locate potential damages that could cause a change in the system’s integrity. Identification can help reduce costs by initiating timely maintenance and extending the structure’s lifetime. Engineers use various types of sensors (e.g. accelerometers, strain gauges, etc.) to assess the structure’s condition. Most systems provide a time series of observations at the sensor’s location. Covering large structures would require the costly installation of multiple sensors and wiring a network for acquisition management. MIMO-SAR, short for Multiple Input Multiple Output Synthetic Aperture Radar, systems are an emerging alternative. By emitting a frequency-modulated continuous wave (FMCW), such systems can use Terrestrial Radar Interferometry (TRI) to measure highly accurately the displacements of an object at high temporal and spatial resolution. However, the effects impacting the performance of MIMO-SAR systems are yet not well understood for practical applications. In this thesis, the applicability of W-band MIMO-SAR for SHM has been investigated. More specifically, the effects impacting the accuracy of a commercial low-cost, automotive MIMO-SAR system have been analysed. Experiments carried out in indoor and outdoor environments under adverse weather conditions have been used to analyse and quantify the impact of measurement noise, short-term drifts due to clock instabilities, meteorological variations, and electromagnetic interference caused by a second active MIMO-SAR system on displacement measurements. This was followed by assessing the capabilities of a MIMO-SAR system for a real-case application, i.e. deformation of a railway bridge under traffic load and deformation of a wind turbine tower under working load. The investigation was rounded off by developing an algorithm to derive 3D displacement vectors from a set of line-of-sight displacements as it is given by TRI. Those algorithms performed least-square adjustments which took into account the spatial or temporal correlations of the observations. The results show that W-Band MIMO-SAR sensors can be used to measure short-term line-of-sight displacement with low uncertainties (tens of micrometres) and high temporal resolution (milliseconds). The system configuration used in these investigations allowed 2D mapping of the displacements of objects located up to 175 metres with high angular (approx. 1.4 degrees) and range (up to 4 centimetres) resolution. Furthermore, measurements acquired by three simultaneously operating MIMO-SAR sensors could be combined to derive 3D displacement vectors coinciding with the actual movement of a point scatterer (corner cube). The investigations expanded the knowledge regarding the performance and quality of the phase measurements of MIMO-SAR systems operating in the W-band. Their applicability for SHM has been demonstrated on two engineering structures. The results indicate that the MIMO-SAR technology could supplement or even replace classical geodetic and other measurement systems used for deformation monitoring.

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