Ionospheric Phase Research Articles

In-field Phasing at the Upgraded GMRT

In time-domain radio astronomy with arrays, voltages from individual antennas are added together with proper delay and fringe correction to form the beam in real time. In order to achieve the correct phased addition of antenna voltages, one has to also correct for the ionospheric and instrumental gains. Conventionally this is done using observations of a calibrator source located near to the target field. This scheme is suboptimal since it does not correct for the variation of the gains with time and position in the sky. Further, since the ionospheric phase variation is typically most rapid at the longest baselines, the most distant antennas are often excluded while forming the beam. We present here a different methodology (“in-field phasing”), in which the gains are obtained in real-time using a model of the intensity distribution in the target field, which overcomes all of these drawbacks. We present observations with the upgraded Giant Metrewave Radio Telescope (uGMRT) which demonstrates that in-field phasing does lead to a significant improvement in sensitivity. We also show, using observations of the millisecond pulsar J1120−3618 that this in turn leads to a significant improvement of measurements of the Dispersion Measure and Time of Arrival. Finally, we present test observations of the GMRT discovered eclipsing black widow pulsar J1544+4937 showing that in-field phasing leads to improvement in the measurement of the cut-off frequency of the eclipse.

Correction of Ionospheric Phase in SAR Interferometry Considering Wavenumber Shift

Correction of Ionospheric Phase in SAR Interferometry Considering Wavenumber Shift

Spreading Sea Clutter Suppression for High-Frequency Hybrid Sky-Surface Wave Radar Using Orthogonal Projection in Spatial–Temporal Domain

In recent years, the high-frequency hybrid sky-surface wave radar (HSSWR) has been increasingly used for target detection applications. Nevertheless, the specific bistatic system layout and the phase path disturbances induced by the ionospheric propagation channel may severely spread the sea clutter spectrum, thereby deteriorating the detection ability of the HSSWR for slow-moving targets. In this work, a novel subspace method based on the hybrid use of the amplitude and phase estimator (APES) and the orthogonal projection (OP) in the spatial–temporal domain, denoted as the APES-OP method, is proposed to suppress the spreading first-order sea clutter of the HSSWR. The distribution characteristics of targets and first-order sea clutter in the spatial–temporal domain were investigated, and a time-domain subspace signal model was adopted to describe targets perturbed by ionospheric phase path modulation. An APES filter was adopted to filter out the potential targets with a traversal approach to avoid attenuating desired signals while suppressing sea clutter. After that, sampling data from multi-channels and slow-time domains at the cell under test were employed to construct a spatial–temporal matrix, which was then utilized to obtain the sea clutter subspace by singular value decomposition. Simulation results indicate that the proposed algorithm can suppress sea clutter while retaining the target, even if the target is buried by sea clutter. The processing results of measured data further demonstrate the efficiency of the proposed algorithm. After sea clutter suppression, the target obscured by clutter can be revealed, and the signal-to-clutter ratio of the target is greatly improved.

A Two-Step Phase Compensation-Based Imaging Method for GNSS-Based Bistatic SAR: Extraction and Compensation of Ionospheric Phase Scintillation

A Two-Step Phase Compensation-Based Imaging Method for GNSS-Based Bistatic SAR: Extraction and Compensation of Ionospheric Phase Scintillation

SAR sensing of the atmosphere: stack-based processing for tropospheric and ionospheric phase retrieval

ABSTRACT This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management.

Classification of Ionospheric Scintillations during high Solar Activity and Geomagnetic Storm over Visakhapatnam Region using Machine Learning Approach

The ionospheric plasma disturbances typically correlate with irregularities in electron density and ionospheric scintillations are produced in reaction to these variations generating radio signal fluctuations. Geolocation services and space based communication are endangered due to ionospheric scintillation which promptly produces fluctuations in information collected by Global Navigation Satellite Systems and this is at its strongest when the solar cycle is at its peak. Ionospheric space weather has a significant impact on Global Navigation Satellite Systems (GNSS) and one crucial aspect used in investigating ionospheric characteristics is total electron content (TEC). Due to fluctuations in time and space, the TEC obtained from GNSS signals is nonlinear and nonstationary. In this study, machine learning approaches for Classification of the ionospheric scintillations were used during the high solar activity and geomagnetic storm in the month of July 2023. This approach enables the classification of ionospheric phase scintillations using well-known classifiers: Decision Tree and Support Vector Machine.

Compensation of Ionospheric Phase Distortion in HF Hybrid Sky-Surface Wave Radar Using Piecewise Polynomial Phase Modeling Method

Compensation of Ionospheric Phase Distortion in HF Hybrid Sky-Surface Wave Radar Using Piecewise Polynomial Phase Modeling Method

Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions

Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.

Remote Sensing of Ocean Dynamics Parameters by Wide‐Beam High‐Frequency Hybrid Sky‐Surface Wave Radar: Localization of Sea Surface Scattering Points and Decontamination of Semi‐Physical Ionosphere Phase

AbstractThis study addresses the issue of locating the scattering points on the sea surface and rectifying the interference caused by the ionosphere phase in the remote sensing of ocean dynamics parameters. Wide‐beam high‐frequency hybrid sky‐surface wave radar (WB‐HFSSWR) was used for this purpose. In order to mitigate the impact of ionosphere phase contamination on the electron density variation along the sky‐wave path of a radio wave, we present two algorithms: the WB‐HFSSWR Single‐Station sea surface scattering point localization algorithm (WB‐SPLA) and the semi‐physical ionosphere phase decontamination algorithm (SP‐IPDA). Compared with the currently employed ionosphere phase decontamination algorithm, the SP‐IPDA algorithm exhibited enhanced physical characteristics. To evaluate the efficacy of the proposed algorithms, we calculate the root‐mean‐square errors (RMSEs) of the elliptical current velocity (vE‐IRI and vE‐GST) of the WB‐HFSSWR, based on the SP‐IPDA and generalized S‐transform algorithm (GST). The results demonstrated that the RMSEs of vE‐IRI and vE‐GST were comparable to the precision of the bistatic surface‐wave radar elliptical current velocity. The data volume of vE‐IRI was 20.5% larger than that of vE‐GST, and vE‐IRI was more precise than vE‐GST, with RMSEs of 11.98 and 12.63 cm/s, respectively. Furthermore, on the common sea surface scattering points, the precision of vE‐IRI was approximately 18.4% higher than that of vE‐GST.

Machine learning approach for prediction of total electron content and classification of ionospheric scintillations over Visakhapatnam region

Ionospheric scintillations, which are due to ionospheric plasma density anomalies, negatively impact trans-ionospheric signals and the positioning accuracy of the global navigation satellite system (GNSS). One of the crucial variables for comprehending space weather conditions is the total electron content (TEC) of the ionosphere. It is vital to predict the ionospheric TEC before making efforts to enhance the GNSS system. In this article, the long short-term memory machine learning approach for TEC prediction is presented, based on which the ionospheric phase scintillations are identified and classified using popular classifiers: support vector machines and decision trees. In this article, the comparative analysis of these classifiers is presented using the standard performance metrics: accuracy, recall, precision, and F1 score.

Compensation of Background Ionospheric Effect on L-Band Geosynchronous SAR with Fully Polarimetric Data

The L-band geosynchronous synthetic aperture radar (GEO-SAR) has been widely praised for its advantages of short revisit time, wide coverage and stable backscattering information acquisition. However, due to the ultra-long integrated time, the echo will be affected by the time-variant background ionosphere, leading in particular to defocusing in the azimuth direction. Existing compensation methods suitable for low Earth orbit SAR (LEO-SAR) are based on the SAR image or the semi-focused image at the ionospheric phase screen, assuming that the ionosphere is time-frozen for a short integrated period; thus, accurate reconstruction of the time-variant characteristics for the ionosphere in GEO-SAR cannot be achieved. In this paper, a compensation method of background ionospheric effects on L-band GEO-SAR with fully polarimetric data is proposed. Considering the continuous variation of the ionosphere within the synthetic aperture, a decompression processing is proposed to reconstruct the echo by recovering the temporal sampling according to the imaging geometry. By virtue of the Faraday rotation angle, the time-variant total electron content (TEC) is accurately estimated with the reconstructed echo. Based on the established error model, the ionospheric effects are well compensated with the estimated TEC. Simulations with the real SAR data from ALOS-2 and the measured time-variant TEC from USTEC validate the effectiveness and performance of the proposed method. The impacts from thermal noise and polarimetric calibration error are also quantitatively analyzed. From this, the error thresholds are given to guarantee compensation accuracy, namely 18.96 dB for SNR, −15.63 dB for crosstalk and −1.02 dB to 0.31 dB for the amplitude of the channel imbalance, and the argument of the channel imbalance is suggested to be maintained as close to zero as possible.

Seasonal Characteristics of Phase Scintillation Observed at UKM Station in Malaysia

We present a statistical comparison of the seasonal occurrence of ionospheric phase scintillation in the GPS signals at an equatorial region in Malaysia using the ground-based ionospheric monitoring GPS receiver and space-based radio occultation (RO) measurements. The observations were recorded at UKM (2.55°N, 101.461°E) in Malaysia during 2010 using GPS based scintillation and TEC Monitoring setup (GISTM). The space-based measurements were recorded by the FORMOSAT_3/COSMIC (F_3/C) satellites in 2010. The GPS limb-viewing radio occultation technique is an innovative technique that provides ideal geometry to observe vertical ionospheric structures to detect Equatorial Plasma bubbles (EPBs) and Sporadic E layers (Es). Radio waves throughout the irregular equatorial ionosphere are studied to characterize phase scintillations at the L1 frequency in different seasons. Seasonal characteristics of the GPS phase scintillation show it mainly occurs in the equinox months, particularly during March. The results show similar values as the Radio Occultation (RO) observations over UKM station, which demonstrates that March equinox has the maximum occurrence of scintillations with the appearance of EPBs and during June corresponding to the appearance of the Es layer in the ionosphere. We found seasonal characteristics of phase scintillations obtained from the Ground-based station are consistent with seasonal characteristics of amplitude scintillation that occurred from the space-based measurements. Nevertheless, F3/C RO provided an opportunity for concurrent sounding of EPBs and Es characteristic structures across the globe, unlike other traditional ground-based measurements.

Correction of ionosphere phase contamination of high‐frequency hybrid sky‐surface wave radar using wavelet transform

AbstractIonospheric phase contamination (IPC) is a major contributor to the imprecise retrieval of ocean dynamics parameters by the high‐frequency hybrid sky‐surface wave radar (HFHSSW). Since the direct wave and sea echo of HFHSSW have comparable IPC, the information of ocean dynamics parameters can be preserved by using the IPC extracted from the direct waves to correct the sea echoes. In this article, a wavelet transform (WT) algorithm for HFHSSW sea echo correction is proposed. The direct waves are decomposed using a series of wavelet functions constructed with complex Morlet wavelets in order to extract the IPC and correct the sea echoes. The authors enumerated two examples and counted the correction results for 50 echoes to validate the performance of the WT algorithm. The results demonstrate that the WT algorithm enhances the signal‐to‐noise ratio (SNR) of the sea echoes and suppresses the broadening of the sea echoes. The 6‐dB bandwidth of the positive and negative first‐order Bragg peaks (B+, B−) decreases by 32.96% and 35.04% respectively, while the SNR increases by 1.16 and 1.21 dB respectively. In comparison to the generalised S‐transform (GST algorithm), the computation speed of the WT algorithm is approximately 3.2 times that of the GST algorithm, making it more suitable for practical operation.

Extracting ionospheric phase scintillation indicator from GNSS observations with 30-s sampling interval in the high-latitude region

Extracting ionospheric phase scintillation indicator from GNSS observations with 30-s sampling interval in the high-latitude region

Spread sea clutter suppression via prior knowledge‐based space time adaptive processing in high frequency hybrid sky‐surface wave radar

AbstractIn high frequency (HF) hybrid sky‐surface wave radar, the first‐order sea clutter broadening is severe under the action of ionospheric phase disturbance and bistatic angles. In this study, a spread sea clutter suppression method based on prior knowledge and joint domain localised (JDL) algorithm is proposed. Firstly, the first‐order sea clutter broadening mechanisms and model under the action of ionospheric phase disturbance and bistatic angles are introduced. Secondly, the space‐time coupling characteristics of the first‐order sea clutter is analysed. Then in order to better suppress clutter or avoid target information loss, the spread sea clutter suppression method based on space time adaptive processing is proposed. In the proposed method, the space‐time distribution characteristics and simulation model of the first‐order sea clutter is used as the prior knowledge into JDL algorithm to improve the sea clutter suppression performance. Finally, the performance of sea clutter suppression based on JDL algorithm is simulated and analysed. And the proposed spread sea clutter suppression method based on prior knowledge and JDL algorithm is proved by measured data.

Joint correction method for ionospheric phase pollution of high‐frequency sky‐surface wave radar based on adaptive optimal path

AbstractThe high‐frequency (HF) hybrid sky‐surface wave system is a new early warning radar. However, due to the influence of bistatic angle and ionospheric phase path disturbance, the echo spectrum will be widened, which seriously affects the ability of detecting slow targets of ships. In view of the overlapping of positive and negative Bragg broadening peaks caused by the large amplitude ionospheric phase path disturbance in the echo spectrum of HF sky‐surface wave radar, a joint correction method of ionospheric phase pollution based on adaptive optimal path is proposed in this paper. Firstly, the time‐frequency spectrum polluted by ionospheric phase is obtained by high resolution time‐frequency MUSIC transform, then the ionospheric phase disturbance curve in the time spectrum of polluted echo is extracted by the proposed adaptive optimal path method, and then the polluted echo is initially compensated to obtain the sharpened ocean echo spectrum. Finally, the residual phase of pollution curve is estimated by the Phase Gradient Autofocus method, and then the pollution echo is accurately corrected. The results of theoretical and measured data show that this method is effective in correcting the overlapping of positive and negative Bragg broadening peaks caused by serious ionospheric phase pollution.

Gulf of Mexico Loop-Current Signature Observed From GNSS-R Phase Altimetry Based on Spire Global CubeSat Data

Global Navigation Satellite System (GNSS) coherent ocean reflections collected at grazing-angles by Spire Global’s CubeSats are explored to retrieve Sea Level Anomalies (SLA) over the Gulf of Mexico. Dual-frequency GPS carrier phase estimations are derived from the 50-Hz I and Q samples generated onboard the low Earth orbit CubeSats. First order ionospheric phase advancement, troposphere delay, and precise orbit solutions for GPS satellites and CubeSats mitigate the range measurement errors. Comparisons between collocated specular reflection tracks from various Spire CubeSats and GPS satellites configurations demonstrate the self-consistency of the resulting SLA solutions. The Spire retrievals are then validated against conventional altimetry SLA products. There is a Root Mean Square (RMS) difference of ~ 20 cm between the Spire and the gridded model mesoscale SLAs, unless the reflected elevations are very low. To better evaluate the performance of the Spire retrievals they are examined over sharp ocean topography. About 40% of coherent reflections with elevations above 12° detected the signature of the energetic Loop-Current during the 2020-2021 period. The Spire retrievals were also compared with collocated Sentinel-3 tracks. One track with elevations above 12° demonstrated high correlation and agreement with the Sentinel-3 retrievals. The other two low-elevation tracks showed large magnitude discrepancies, but similar trends once appropriate scaling factors are applied. The study demonstrates the potential for GNSS reflectometry’s application to SLA mapping while also highlighting where significant improvements in error mitigations are needed. A particularly large error source is the troposphere error for signals with lower elevations.

Ionospheric Phase Delay Correction for Time Series Multiple-Aperture InSAR Constrained by Polynomial Deformation Model

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

Compensation for the ionospheric dispersion effect on spaceborne P-band full-polarimetric wideband SAR

ABSTRACT Spaceborne P-band full-polarimetric wideband synthetic aperture radar (SAR) will be affected by ionospheric phase dispersion and polarimetric dispersion. An improved contrast optimization autofocus (COA) method using four polarization channel data is proposed to compensate for both phase dispersion and polarimetric dispersion. The basis and steps of the method are given. Its performance is analysed by typical target simulation under clutter background and compared with traditional COA method for single channel. The residual quadratic phase error (QPE) of single channel compensated by traditional method may exceed 45° focusing threshold, while the proposed method can reduce the error to less than 10°. Due to the lack of spaceborne P-band SAR data, we simulate the effect of ionosphere on P-band SAR based on ALOS2 PALSAR2 data and verify the proposed method. The simulation result shows the proposed method is effective and superior to traditional method.

Ionospheric Phase Scintillation Index Estimation Based on 1 Hz Geodetic GNSS Receiver Measurements by Using Continuous Wavelet Transform

AbstractThe adverse effect of the ionospheric scintillation on Global Navigation Satellite System (GNSS) requires scintillation monitoring on a global scale. Ionospheric Scintillation Monitoring Receivers (ISMR) are usually adopted to monitor scintillation, while they are not suitable for global monitoring due to the 50 Hz data collecting rate, which restricts the distribution. This paper proposes a new method to extract the phase scintillation index from each GNSS carrier with 1s‐sampling‐interval, mainly based on the cycle slip detection, the geodetic detrending and the wavelet transform, in which the optimal symmetry parameter and the time‐bandwidth product are determined with trial calculation. Taken the index provided by ISMR as the reference, 1‐year observations are utilized to evaluate the scintillation monitoring performance of the extracted index regarding the correlation of the magnitude in each observation arc, the detected daily scintillation occurrence rate, the diurnal variation pattern of the ionospheric scintillation, the correlation between the scintillation occurrence rate and the space weather parameter, and the complementary cumulative distribution of the magnitudes. Compared to the performance of Rate of Total electron content Index, a higher consistency can be achieved between the extracted index and the index, indicating the rationality of applying the proposed method in monitoring scintillations. The extracted scintillation index can be expected to introduce geodetic receivers operating at 1s‐sampling‐interval into the field of ionospheric scintillation monitoring on a global scale.