Implication between Geophysical Events and the Variation of Seasonal Signal Determined in GNSS Position Time Series

The seasonal signal determined by the Global Navigation Satellite System (GNSS), which is captured in the coordinate time series, exhibits annual and semi-annual periods. This signal is frequently modelled by two periodic signals with constant amplitude and phase-lag. The purpose of this study is to explore the implication of different types of geophysical events on the seasonal signal in three stages—in the time span that contains the geophysical events, before and after the geophysical event, but also the stationarity phenomena, which is analysed on approximately 200 reference stations from the EPN network since 1995. The novelty of the article is demonstrated by correlating three different types of geophysical events, such as earthquakes with a magnitude greater than 6° on the Richter scale, landslides, and volcanic activity, and analysing the variation in amplitude of the seasonal signal. The geophysical events situated within a radius of 30 km from the epicentre showed a higher seasonal value than when the timespan did not contain a geophysical event. The presence of flicker and random walk noise was computed using overlapping Hadamard variance (OHVAR) and the non-stationary behaviour of the time series of the CORS coordinates in the time frequency analysis was done using continuous wavelet transform (CWT).

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Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

Remote Sensing ◽

10.3390/rs12060992 ◽

2020 ◽

Vol 12 (6) ◽

pp. 992 ◽

Cited By ~ 1

Author(s):

Kunpu Ji ◽

Yunzhong Shen ◽

Fengwei Wang

Keyword(s):

Time Series ◽

Wavelet Analysis ◽

Satellite System ◽

Signal Extraction ◽

Average Error ◽

North East ◽

Observation Network ◽

Position Time Series ◽

Global Navigation Satellite ◽

Gnss Position Time Series

The daily position time series derived by Global Navigation Satellite System (GNSS) contain nonlinear signals which are suitably extracted by using wavelet analysis. Considering formal errors are also provided in daily GNSS solutions, a weighted wavelet analysis is proposed in this contribution where the weight factors are constructed via the formal errors. The proposed approach is applied to process the position time series of 27 permanent stations from the Crustal Movement Observation Network of China (CMONOC), compared to traditional wavelet analysis. The results show that the proposed approach can extract more exact signals than traditional wavelet analysis, with the average error reductions are 13.24%, 13.53% and 9.35% in north, east and up coordinate components, respectively. The results from 500 simulations indicate that the signals extracted by proposed approach are closer to true signals than the traditional wavelet analysis.

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Independent Component Extraction from the Incomplete Coordinate Time Series of Regional GNSS Networks

Sensors ◽

10.3390/s21051569 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1569

Author(s):

Tengfei Feng ◽

Yunzhong Shen ◽

Fengwei Wang

Keyword(s):

Time Series ◽

Missing Data ◽

Satellite System ◽

Independent Component ◽

Independent Components ◽

North East ◽

The North ◽

Before And After ◽

Original Time ◽

Global Navigation Satellite

Independent component analysis (ICA) is one of the most effective approaches in extracting independent signals from a global navigation satellite system (GNSS) regional station network. However, ICA requires the involved time series to be complete, thereby the missing data of incomplete time series should be interpolated beforehand. In this contribution, a modified ICA is proposed, by which the missing data are first recovered based on the reversible property between the original time series and decomposed principal components, then the complete time series are further processed with FastICA. To evaluate the performance of the modified ICA for extracting independent components, 24 regional GNSS network stations located in North China from 2011 to 2019 were selected. After the trend, annual and semiannual terms were removed from the GNSS time series, the first two independent components captured 17.42, 18.44 and 17.38% of the total energy for the North, East and Up coordinate components, more than those derived by the iterative ICA that accounted for 16.21%, 17.72% and 16.93%, respectively. Therefore, modified ICA can extract more independent signals than iterative ICA. Subsequently, selecting the 7 stations with less missing data from the network, we repeatedly process the time series after randomly deleting parts of the data and compute the root mean square error (RMSE) from the differences of reconstructed signals before and after deleting data. All RMSEs of modified ICA are smaller than those of iterative ICA, indicating that modified ICA can extract more exact signals than iterative ICA.

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Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Remote Sensing ◽

10.3390/rs14010017 ◽

2021 ◽

Vol 14 (1) ◽

pp. 17

Author(s):

Pia Ruttner ◽

Roland Hohensinn ◽

Stefano D’Aronco ◽

Jan Dirk Wegner ◽

Benedikt Soja

Keyword(s):

Time Series ◽

Meteorological Data ◽

Reduction Rate ◽

Satellite System ◽

Physical Models ◽

Convolutional Network ◽

Position Time Series ◽

Environmental Surface ◽

Meteorological Features ◽

Global Navigation Satellite

Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.

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An Innovative Approach to Accuracy of Co-Seismic Surface Displacement Detection Using Satellite GNSS Technology

Applied Sciences ◽

10.3390/app11062800 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2800

Author(s):

Hana Staňková ◽

Jakub Kostelecký ◽

Miroslav Novosad

Keyword(s):

Time Series ◽

Surface Displacement ◽

Satellite System ◽

Dual Approach ◽

Seismic Displacement ◽

Accuracy Of Measurements ◽

Before And After ◽

Using Data ◽

Global Navigation Satellite ◽

Seismic Models

This paper discusses a new method for determining co-seismic displacement using the Global Navigation Satellite System (GNSS) for the precise detection of positional changes at permanent stations after an earthquake. Positioning by the Precise Point Positioning (PPP) method is undertaken using data from the GNSS satellites and one designated station. A time series is processed by an anharmonic analysis before and after an earthquake and these one-day solutions increase the accuracy of measurements. The co-seismic static displacement can be precisely detected from the analysed time series before and after the earthquake, which can be used for the verification of seismic models. Reliability of the estimation of the size of the co-seismic offset is given by the mean square error (RMSE) of the shift. In this study, RMSE was determined by two approaches, initially from variances within PPP processing, and secondly when no positional change from the GNSS before or after the earthquake was assumed. The variance of the data in the time series gives a more realistic estimate of RMSE. This dual approach can affect seismological interpretation due to the need for the interpreting geophysicists to determine which case of co-seismic displacement is more probable for any given locality. The second approach has been shown to provide a more realistic co-seismic displacement accuracy in this study.

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Investigation of GIM-TEC disturbances before M ≥ 6.0 inland earthquakes during 2003–2017

Scientific Reports ◽

10.1038/s41598-020-74995-w ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Fuying Zhu ◽

Yingchun Jiang

Keyword(s):

Rapid Development ◽

Science Research ◽

Satellite System ◽

Statistical Investigation ◽

Electron Content ◽

Total Electron ◽

Spatial And Temporal Distributions ◽

Before And After ◽

Global Ionosphere Map ◽

Global Navigation Satellite

Abstract With the rapid development of the Global Navigation Satellite System (GNSS) and its wide applications to atmospheric science research, the global ionosphere map (GIM) total electron content (TEC) data are extensively used as a potential tool to detect ionospheric disturbances related to seismic activity and they are frequently used to statistically study the relation between the ionosphere and earthquakes (EQs). Indeed, due to the distribution of ground based GPS receivers is very sparse or absent in large areas of ocean, the GIM-TEC data over oceans are results of interpolation between stations and extrapolation in both space and time, and therefore, they are not suitable for studying the marine EQs. In this paper, based on the GIM-TEC data, a statistical investigation of ionospheric TEC variations of 15 days before and after the 276 M ≥ 6.0 inland EQs is undertaken. After eliminating the interference of geomagnetic activities, the spatial and temporal distributions of the ionospheric TEC disturbances before and after the EQs are investigated and compared. There are no particularly distinct features in the time distribution of the ionospheric TEC disturbances before the inland EQs. However, there are some differences in the spatial distribution, and the biggest difference is precisely in the epicenter area. On the other hand, the occurrence rates of ionospheric TEC disturbances within 5 days before the EQs are overall higher than those after EQs, in addition both of them slightly increase with the earthquake magnitude. These results suggest that the anomalous variations of the GIM-TEC before the EQs might be related to the seismic activities.

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Performance Assessment of GNSS-R Polarimetric Observations for Sea Level Monitoring

10.5194/egusphere-egu21-12709 ◽

2021 ◽

Author(s):

Mahmoud Rajabi ◽

Mstafa Hoseini ◽

Hossein Nahavandchi ◽

Maximilian Semmling ◽

Markus Ramatschi ◽

...

Keyword(s):

Time Series ◽

Circular Polarization ◽

Sea Level ◽

Tide Gauge ◽

Satellite System ◽

Sea Surface ◽

Analysis Tool ◽

Lower Accuracy ◽

Coastal Sea ◽

Global Navigation Satellite

Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge. &#160;We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15&#160;minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.

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GNSS Multipath Detection Using Continuous Time-Series C/N0

Sensors ◽

10.3390/s20144059 ◽

2020 ◽

Vol 20 (14) ◽

pp. 4059

Author(s):

Nobuaki Kubo ◽

Kaito Kobayashi ◽

Rei Furukawa

Keyword(s):

Time Series ◽

Continuous Time ◽

Urban Areas ◽

Satellite System ◽

Line Of Sight ◽

Static Tests ◽

Gnss Receivers ◽

Multipath Detection ◽

Global Navigation Satellite ◽

Multipath Errors

The reduction of multipath errors is a significant challenge in the Global Navigation Satellite System (GNSS), especially when receiving non-line-of-sight (NLOS) signals. However, selecting line-of-sight (LOS) satellites correctly is still a difficult task in dense urban areas, even with the latest GNSS receivers. This study demonstrates a new method of utilization of C/N0 of the GNSS to detect NLOS signals. The elevation-dependent threshold of the C/N0 setting may be effective in mitigating multipath errors. However, the C/N0 fluctuation affected by NLOS signals is quite large. If the C/N0 is over the threshold, the satellite is used for positioning even if it is still affected by the NLOS signal, which causes the positioning error to jump easily. To overcome this issue, we focused on the value of continuous time-series C/N0 for a certain period. If the C/N0 of the satellite was less than the determined threshold, the satellite was not used for positioning for a certain period, even if the C/N0 recovered over the threshold. Three static tests were conducted at challenging locations near high-rise buildings in Tokyo. The results proved that our method could substantially mitigate multipath errors in differential GNSS by appropriately removing the NLOS signals. Therefore, the performance of real-time kinematic GNSS was significantly improved.

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A Joint Method Based on Time-Frequency Distribution to Detect Time-Varying Interferences for GNSS Receivers with a Single Antenna

Sensors ◽

10.3390/s19081946 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1946 ◽

Cited By ~ 1

Author(s):

Qingshui Lv ◽

Honglei Qin

Keyword(s):

Hough Transform ◽

Frequency Estimation ◽

Satellite System ◽

Time Varying ◽

Actual Performance ◽

Time Frequency ◽

Single Antenna ◽

Global Navigation Satellite ◽

Mean Square Errors ◽

Joint Method

In this paper, a joint method combining Hough transform and reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) is presented to detect time-varying interferences with crossed frequency for a Global Navigation Satellite System (GNSS) receiver with a single antenna. The proposed method can prevent the cross-term interference and detect the time-varying interferences with crossed frequency which cannot be achieved by the classical time-frequency (TF) analysis with the peak detection method. The actual performance of the developed method has been evaluated by experiments with conditions where the real BeiDou system (BDS) B1I signals are corrupted by the simulated chirp interferences. The results of experiments show that the introduced method is effectively able to detect chirp interferences with crossed frequency and provide the same root mean square errors (RMSE) of the parameter estimation for chirp one and the improved initial frequency estimation for chirp two compared with the Hough transform of Wigner-Ville distribution (WVD) when the jamming to noise ratio (JNR) equals or surpasses 4 dB.

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Space-Time-Frequency Adaptive Processor for Multiple Interference Suppression in GNSS Applications

International Journal of Antennas and Propagation ◽

10.1155/2018/2301052 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Qiang Guo ◽

Lian-gang Qi ◽

Jianhong Xiang

Keyword(s):

Interference Suppression ◽

Satellite System ◽

Space Time ◽

Similar Frequency ◽

Time Frequency ◽

Weight Calculation ◽

Global Navigation Satellite ◽

Short Time ◽

Multiple Interference ◽

Adaptive Processor

To enhance the multiple interference suppression performance of global navigation satellite system (GNSS) receivers without extra antenna elements, a space-time-frequency adaptive processor (STFAP) is investigated. Firstly, based on the analysis of the autocorrelation function of the multicomponent signal, we propose a common period estimation and data block technique to segment the received signal data into blocks. Secondly, the signal data in each block are short-time Fourier transformed into time-frequency (TF) domain, and the corresponding TF points with similar frequency characteristics are regrouped to structure space-time-frequency (STF) data matrixes. Finally, a space-time-frequency minimum output power- (STF-MOP) based weight calculation method is introduced to suppress multiple interfering signals according to their sparse characteristics in TF and space domains. Simulation results show that the proposed STFAP can effectively combat more wideband periodic frequency-modulated (WBPFM) interferences even some of them arriving from the same direction as GNSS signals without increasing the number of antenna elements.

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VISUAL ANALYSIS OF RECURRENCE OF TIME SERIES OF THE COORDINATES ENU IN THE GPS STATIONS

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702018000400029 ◽

2018 ◽

Vol 24 (4) ◽

pp. 470-484

Author(s):

Alfonso Tierra ◽

Rubén León ◽

Alexis Tinoco-S ◽

Carolina Cañizares ◽

Marco Amores ◽

...

Keyword(s):

Time Series ◽

Continuous Monitoring ◽

Visual Analysis ◽

Finite Impulse Response ◽

Satellite System ◽

Nearest Neighbours ◽

Low Pass ◽

Global Navigation Satellite ◽

Low Pass Filters ◽

Content Information

Abstract The time series content information about the dynamic behavior of the system under study. This behavior could be complex, irregular and no lineal. For this reason, it is necessary to study new models that can solve this dynamic more satisfactorily. In this work a visual analysis of recurrence from time series of the coordinate’s variation ENU (East, North, Up) will be made. This analysis was obtained from nine continuous monitoring stations GPS (Global Navigation Satellite System); the intention is to study their behavior, they belong to the Equatorian GPS Network that materializes the reference system SIRGAS - ECUADOR. The presence of noise in the observations was reduced using digital low pass filters with Finite Impulse Response (FIR). For these series, the time delay was determined using the average mutual information, and for the minimum embedding dimension the False Nearest Neighbours (FNN) method was used; the purpose is to obtain the recurrent maps of each coordinates. The results of visual analysis show a strong tendency, especially in the East and North coordinates, while the Up coordinates indicate discontinued, symmetric and periodic behavior.

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