Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations.

[1]  Lawrence Lau,et al.  Wavelet packets based denoising method for measurement domain repeat-time multipath filtering in GPS static high-precision positioning , 2017, GPS Solutions.

[2]  Ahmed El-Mowafy,et al.  Evaluation of High-Rate GNSS-PPP for Monitoring Structural Health and Seismogeodesy Applications , 2018 .

[3]  Xingxing Li,et al.  Real-time high rate GNSS techniques for earthquake monitoring and early warning , 2015 .

[4]  Mosbeh R. Kaloop,et al.  Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests , 2018 .

[5]  Mosbeh R. Kaloop,et al.  Time-series analysis of GPS measurements for long-span bridge movements using wavelet and model prediction techniques , 2019, Advances in Space Research.

[6]  C. Lindsay Anderson,et al.  A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds , 2016, IEEE Access.

[7]  Jean-Michel Poggi,et al.  Multivariate denoising using wavelets and principal component analysis , 2006, Comput. Stat. Data Anal..

[8]  Yongyi Zhang,et al.  GNSS-Based Verticality Monitoring of Super-Tall Buildings , 2018 .

[9]  Fanis Moschas,et al.  Dynamic multipath in structural bridge monitoring: an experimental approach , 2013, GPS Solutions.

[10]  Fanis Moschas,et al.  Dynamic Deflections of a Stiff Footbridge Using 100-Hz GNSS and Accelerometer Data , 2015 .

[11]  Yingwang Xiao Process Monitoring Based on Improved Principal Component Analysis , 2016 .

[12]  Gérard Lachapelle,et al.  GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques , 2018, Sensors.

[13]  Jong Wan Hu,et al.  Damage Identification and Performance Assessment of Regular and Irregular Buildings Using Wavelet Transform Energy , 2016 .

[14]  Jong Wan Hu,et al.  Evaluation of the high-rate GNSS-PPP method for vertical structural motion , 2018, Survey Review.

[15]  Clement Ogaja,et al.  TEQC multipath metrics in MATLAB , 2007 .

[16]  Jong Wan Hu,et al.  Structural Performance Assessment Based on Statistical and Wavelet Analysis of Acceleration Measurements of a Building during an Earthquake , 2016 .

[17]  Karol Dawidowicz Sub-hourly precise point positioning accuracy analysis – case study for selected ASG-EUPOS stations , 2020 .

[18]  Gethin Wyn Roberts,et al.  Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning , 2018, Remote. Sens..

[19]  Gethin Wyn Roberts,et al.  Wavelet De-noising of GNSS Based Bridge Health Monitoring Data , 2014 .

[20]  Hoon Sohn,et al.  Development of a High Precision Displacement Measurement System by Fusing a Low Cost RTK-GPS Sensor and a Force Feedback Accelerometer for Infrastructure Monitoring , 2017, Sensors.

[21]  Jacinta Chan Phooi M’ng,et al.  Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models , 2016, PLoS ONE.

[22]  Hao Wu,et al.  A wavelet-based hybrid approach to remove the flicker noise and the white noise from GPS coordinate time series , 2015, GPS Solutions.

[23]  Caijun Xu,et al.  A data-driven approach for denoising GNSS position time series , 2018, Journal of Geodesy.

[24]  Ahmed El-Mowafy,et al.  Quality Control in Using GNSS CORS Network for Monitoring Plate Tectonics: A Western Australia Case Study , 2016 .

[25]  Xiaoji Niu,et al.  High-rate precise point positioning (PPP) to measure seismic wave motions: an experimental comparison of GPS PPP with inertial measurement units , 2013, Journal of Geodesy.

[26]  Aboelmagd Noureldin,et al.  Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features , 2006 .

[27]  Chalermchon Satirapod,et al.  Analysis of high-frequency multipath in 1-Hz GPS kinematic solutions , 2007 .

[28]  Yu Qian,et al.  Process monitoring based on wavelet packet principal component analysis , 2003 .

[29]  M. Moazedi,et al.  Novel Anti-spoofing Methods Based on Discrete Wavelet Transform in the Acquisition and Tracking Stages of Civil GPS Receiver , 2018, Int. J. Wirel. Inf. Networks.

[30]  Ye Dong,et al.  A New Wavelet Threshold Function and Denoising Application , 2016 .

[31]  N. Quesada-Olmo,et al.  Real-time high-rise building monitoring system using global navigation satellite system technology , 2018, Measurement.

[32]  R. Merry,et al.  Wavelet theory and applications : a literature study , 2005 .

[33]  Fanis Moschas,et al.  Noise characteristics of high-frequency, short-duration GPS records from analysis of identical, collocated instruments , 2013 .

[34]  Harald Schuh,et al.  Mitigation of Unmodeled Error to Improve the Accuracy of Multi-GNSS PPP for Crustal Deformation Monitoring , 2019, Remote. Sens..

[35]  Caijun Xu,et al.  Denoising effect of multiscale multiway analysis on high-rate GPS observations , 2015, GPS Solutions.

[36]  Gethin Wyn Roberts,et al.  Real-time kinematic PPP GPS for structure monitoring applied on the Severn Suspension Bridge, UK , 2017 .

[37]  Jong Wan Hu,et al.  Recent Advances of Structures Monitoring and Evaluation Using GPS-Time Series Monitoring Systems: A Review , 2017, ISPRS Int. J. Geo Inf..

[38]  Ji Ma,et al.  Application and Optimization of Wavelet Transform Filter for North-Seeking Gyroscope Sensor Exposed to Vibration , 2019, Sensors.

[39]  B. Bakshi Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[40]  Fanis Moschas,et al.  Strong motion displacement waveforms using 10‐Hz precise point positioning GPS: an assessment based on free oscillation experiments , 2014 .

[41]  Denghui Wang,et al.  Multipath extraction and mitigation for bridge deformation monitoring using a single-difference model , 2017 .

[42]  Lu Ke Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet , 2017 .

[43]  Marco Alberto Javarone,et al.  Modeling Radicalization Phenomena in Heterogeneous Populations , 2015, PloS one.

[44]  Cemal Ozer Yigit,et al.  Experimental testing of high-rate GNSS precise point positioning (PPP) method for detecting dynamic vertical displacement response of engineering structures , 2017 .

[45]  Xiaoji Niu,et al.  Real-time Kinematic Positioning of INS Tightly Aided Multi-GNSS Ionospheric Constrained PPP , 2016, Scientific Reports.

[46]  Mosbeh R. Kaloop,et al.  GPS Performance Assessment of Cable-Stayed Bridge using Wavelet Transform and Monte-Carlo Techniques , 2018, KSCE Journal of Civil Engineering.

[47]  Mosbeh R. Kaloop,et al.  Assessment of acceleration responses of a railway bridge using wavelet analysis , 2017 .

[48]  Hao Ye,et al.  Measurement of Bridge Dynamic Responses Using Network-Based Real-Time Kinematic GNSS Technique , 2016 .

[49]  Salih Alcay,et al.  Displacement monitoring performance of relative positioning and Precise Point Positioning (PPP) methods using simulation apparatus , 2019, Advances in Space Research.