Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications
Abstract
:1. Introduction
2. A Review of Spaceborne GNSS-R Missions
3. Spaceborne GNSS-R Raw IF Data and Processing
3.1. GNSS-R Raw IF Data from Different Missions
3.2. Raw IF Data Processing
3.2.1. Direct Signal Processing
3.2.2. Open-Loop Tracking Model Computation
3.2.3. Remarks for Raw IF Processing
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- The navigation signals transmitted by modernized GNSS satellites, e.g., Galileo E1 B/C and BDS-3 B1C, consist of both data and pilot components. The complex waveform for each signal component can be generated independently by cross-correlating the reflected signal to its PRN code. However, instead of the complex waveform for each signal component, the combined complex waveform are generated by cross-correlating the reflected signal to the composite PRN codes of these signal components. For Galileo E1 signal, both E1B and E1C components are in-phase modulated and the composite code are generated by [67]
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- Due to the limitation of the processing time and storage capability, only the complex waveform corresponding to the zero Doppler bin are included in the data products. Nevertheless, the software receiver is also capable of generating the complex DDM with configurable delay and Doppler ranges and resolutions. Moreover, the complex waveforms are generated only with the forward scattering configuration, i.e., corresponding to the signal reflected around the specular point. However, the waveform or DDM can be also generated for the signal scattered from other directions (e.g., backscattering in [48]) or staring at fixed surface regions (e.g., in [34]) by only tuning the open-loop tracking model.
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- The complex waveform is generated with a fixed coherent integration time of . As the complex waveform includes both phase and amplitude information, it is possible to further integrate them coherently with longer coherent integration time. Such configuration can facilitate to characterize the coherence of the reflected signal at different surface conditions and different bistatic geometry (e.g., elevation and azimuth).
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- The above-mentioned processing scheme is only for the processing of the reflected signal at one single frequency band. For the direct and reflected signal from the second frequency band, e.g., GPS L2 signal collected by SPIRE RO satellites and BDS-3 B1I signal collected by the BuFeng-1 satellites, the same processing scheme is applied by only changing the PRN code and carrier frequency parameters. For the processing of multi-frequency GNSS-R data, the complex waveforms from different frequencies are synchronized by using the same start time in (1) for the cross-correlations.
4. Data Products and Processing
4.1. Currently Available Data Products
4.2. Format and Data Structure
4.3. Accessing and Processing of the Data Product
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- Download_cWF_File_List: In addition to the complex waveform netCDF files, the lists of the product tracks are also available at the server. This function is to download the up-to-date lists of the complex waveform products, which include the basic information of the raw IF tracks, such as the raw IF data ID, the data collection time, the PRN of the GNSS transmitter, the SNR of the direct and reflected signals, and the geolocation of the specular point.
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- Find_RawIF_Track: With the basic information of all available tracks, this function is to search complex waveform tracks by time and geographic location, which returns a text file including a list of the complex waveform files with the specular point crossing the target area, i.e., a rectangle area defined by the maximum/minimum latitudes and longitudes.
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- Download_cWF_File: This function is to download a local copy of a complex waveform file from the data server. With the text file returned by the Find_RawIF_Track function, all the complex waveform files can be downloaded sequentially.
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- Read_CW_File: Given the filename of the complex waveform file, this function can return two labeled multi-dimensional arrays including all the variables in the “cWF” group and the “MetaData” group, respectively.
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- ShiftCW and CounterRotateCW: As the OL tracking model (Section 3.2.2) is computed without considering the variation of the surface elevation along the track, it is necessary to realign the complex waveform by recomputing the bistatic delay of the reflected signal following a refined surface elevation model. With the precise estimation of the delay evolution along the track, ShiftCW function is to align the complex waveforms by shifting each of them along the delay axis, and CounterRotateCW function is to counter rotate the phase of the complex waveform by means of a product with a phasor rotated with the corresponding delay difference. It is noted that some other delay correction terms, such as the ionosphere delay and the troposphere delay can be also included in the bistatic delay computation and applied in the complex waveform realignment.
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- Integration: In the raw IF processing stage (Section 3.2), the direct and reflected signals are integrated coherently for a fixed period of 1 ms. To decrease the impact of thermal and speckle noise, coherent integration (complex sum) and incoherent average (averaging of the squared amplitudes) are applied to the 1-ms complex waveforms of the direct and reflected signals by
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- GNSSR_Obs: Different GNSS-R observables can be derived from the complex or power waveforms. Currently, the main observables provided with the example function include the SNRs of the direct and reflected signals, the carrier phase of the reflected signal () and the coherence factor of the reflected signal (). In addition, the geographic locations, i.e., the latitude and longitude, corresponding to these variables are also interpolated at each observation epoch.
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- Visualization: With the value and geographic location of each observable, this function is to generate a Keyhole Markup Language (KML) file using the Python package “simplekml” [70]. By importing the generated KML file into an Earth browser such as Google Earth, the values of the observable can be indicated with color scales along the track of the specular points, which can be used to characterize the GNSS-R observable over different surface types.
5. Discussion on Potential Applications of the Data Products
5.1. Spaceborne GNSS-R from Different GNSS Constellation and Frequency Bands
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- As the outputs of the raw IF data processing, the prototype receiver processing algorithms (e.g., the combination of these GNSS signals with multiplexing structures) have been applied to the reflected signal samples. Thus, the complex waveforms themselves cannot be used directly for the developments of low-layer signal processing algorithms for multi-constellation, multi-frequency GNSS-R. However these intermediate results (e.g., code phases and carrier frequencies/phases of the reflected signals) provided in the data product can facilitate the users to re-process the raw IF data by using advanced signal processing algorithms even without the direct signal processing and the computation of the open-loop tracking model.
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- These complex waveforms are integrated coherently for a relative short integration time (i.e., 1 ms), which can be used to assess the optimal coherent integration times for different GNSS-R signals. In fact in [71], by using the complex waveform products, it is clearly shown that the SNR of the reflected signal can be further improved by up to 1.2 dB for Galileo E1 B/C and BDS-3 B1C signal by increasing the coherent integration time to 4 ms. However, it still needs further investigation how the coherent integration times for different GNSS-R signals can be further optimized as the functions of GNSS-R geometry (e.g., elevation and azimuth angles) and reflecting surface (e.g., open ocean, land, sea ice or ice sheet).
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- The complex waveform products from the other GNSS constellations can be used to assess the performance of these new GNSS signals in different applications (e.g., in [27,71,72]). Such assessments are of great importance for the definition and optimization of mission and instrument specifications for future spaceborne GNSS systems, e.g., in establishing the link budget for reflected signals from different GNSS constellations. Moreover, the combinations of these simultaneous GNSS-R measurements at different frequencies (e.g., as shown in Figure 6d,e) can also be further investigated, which can improve the performances of the GNSS-R retrievals. For example, the combination of the L1 and L2 delay observations (phase delay or group delay) can be used to estimate the ionosphere delay for precise GNSS-R altimetry [18] or ionospheric total electron content measurement [73]. In addition, the combination of their power or amplitude observations at different frequencies can provide a more robust way to estimate the surface bistatic radar cross section or reflectivity, which can be also attempted in future studies.
5.2. Surface Characterization Using Coherence of the Reflected Signal
5.3. Demonstration of GNSS-R Altimetry
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- The first step is to compute the delay of the reflected signal from the group delay or carrier phase observables. The group delay can be estimated from the incoherently averaged power waveform (i.e., in (7)) through waveform retracking, which is to determine the position of the specular point in the waveform window. For the incoherent reflections from rough ocean, the determination of the specular point relies on proper modeling of the reflected waveform as introduced in [26]. On the other hand, for the GNSS signals reflected coherently from smooth surfaces (e.g., lakes or sea ice), the position of the specular point can be assumed to be the peak of the power waveform. Once the position of the specular point is determined in the waveform window, the group delay residual relative to the OL delay can be computed from the variable “delay_of_bin”. The observed bistatic delay of the reflected signal can be computed throughFor the coherent reflections, the carrier phase residual can be computed from the peak of the coherently integrated complex waveform byThe bistatic carrier phase delay can be computed directly from the unwrapped carrier phase residual by
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- The second step is to compute the modeled bistatic delay by considering the different propagation delay corrections by
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- The last step is to retrieve the surface height using the measured and modeled bistatic delay by
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Mission | Freq Band | BW | QP | Length | # RF CHs (UP/DW) | Geog. Dist in Lat | ||
---|---|---|---|---|---|---|---|---|
TDS-1 | L1 | 4.188 | ∼2.5–4.1 | ∼16 | 2 bits, I | 60–90 | 1/1 | [−90, 90] |
CYGNSS | L1 | 3.8724 | ∼2.5 | ∼16 | 2 bits, I | 30–60 | 1/2 | ∼[−45, 45] |
BF-1 | L1 | 0 | ∼2.0 | ∼4 | 2 bits, I&Q | 12 | 1/2 | ∼[−55, 55] |
B1I | 0 | ∼4.0 | ∼8 | |||||
SPIRE RO | L1 | ∼1.6 | ∼2.0 | ∼6 | 2 bits, I | 60–120 | 2/2 | [−90, 90] |
L2 | ∼−1.6 | ∼2.0 | ∼6 |
Group | Variable | Dim | Description | Unit | Symbol |
---|---|---|---|---|---|
cWF | coh_int_time | - | Coherent integration time of the direct and reflected signal | Second | in (1) |
delay_of_bin | lag | Range delays of the complex waveform lags with respect to the specular point | Meter | in (1) | |
Start_time | time | Start time of the cross-correlation, which is used as the time tag of each complex waveform | Second | in (1) | |
Start_sample | time | Start raw IF sample index of the cross-correlation in (1) | - | ||
d_Code_Phase | time | Code phase of the local code replica for the direct signal, estimated from the code tracking loop and interpolated at | Chip | in (3) | |
d_Doppler | time | Carrier Doppler of the local carrier replica for the direct signal, estimated from the carrier tracking loop and interpolated at | Hz | in (3) | |
d_Phase | time | Carrier phase of the local carrier replica for the direct signal, estimated from the carrier tracking loop and interpolated at , | Cycle | in (3) | |
bistatic_delay | time | Additional range delay of the reflected signal with respect to the direct one, interpolated at | Meter | in (2) | |
r_Code_Phase | time | Code phase of the reflected signal, which is interpolated at and applied to the local code replica generation | Chip | in (1) and (3) | |
r_Doppler | time | Carrier Doppler of the reflected signal, which is interpolated at and applied to the local carrier replica generation | Hz | in (1) and (3) | |
r_Phase | time | Carrier phase of the reflected signal, which is interpolated at and applied to the local carrier replica generation | Cycle | in (1) and (3) | |
wf_up_i | time, lag | Complex waveform for the direct signal (Inphase) | - | - | |
wf_up_q | time, lag | Complex waveform for the direct signal (Quadrature) | - | - | |
wf_dw_i | time, lag | Complex waveform for the reflected signal (Inphase) | - | ||
wf_dw_q | time, lag | Complex waveform for the reflected signal (Quadrature) | - | ||
MetaData | MetaTime | time | Reference time of the metadata | GPS SoW | in (2) |
x_rcv, y_rcv, z_rcv | time | Position of the receiver in WGS-84 coordinate at | Meter | in (2) | |
x_tx, y_tx, z_tx | time | Position of the transmitter in WGS-84 coordinate at | Meter | in (2) | |
x_sp, y_sp, z_sp | time | Position of the specular point in WGS-84 coordinate at | Meter | in (2) |
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Li, W.; Cardellach, E.; Ribó, S.; Oliveras, S.; Rius, A. Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications. Remote Sens. 2022, 14, 1344. https://doi.org/10.3390/rs14061344
Li W, Cardellach E, Ribó S, Oliveras S, Rius A. Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications. Remote Sensing. 2022; 14(6):1344. https://doi.org/10.3390/rs14061344
Chicago/Turabian StyleLi, Weiqiang, Estel Cardellach, Serni Ribó, Santi Oliveras, and Antonio Rius. 2022. "Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications" Remote Sensing 14, no. 6: 1344. https://doi.org/10.3390/rs14061344