Reflectometry With an Open-Source Software GNSS Receiver: Use ...

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Reflectometry With an Open-Source Software GNSS Receiver: Use Case With Carrier Phase Altimetry Laurent Lestarquit, Mathieu Peyrezabes, Jos´e Darrozes, Erwan Motte, Nicolas Roussel, Gilles Wautelet, Fr´ed´eric Frappart, Guillaume Ramillien, Richard Biancale, and Mehrez Zribi

Abstract—An open-source GNSS software receiver allows to have full access to the signal processing and to make add-ons to the source code in order to obtain the desired GNSS reflectometry processing. The direct signal is processed in the standard way, its tracking loops replica are tapped to have a robust processing of the reflected signal in a master-slave configuration, with the very same carrier replica used to correlate the reflected signal. In addition, the data bit sign is wiped off, which allows to extend the coherent integration time (CIT) well beyond the usual 20-ms limit on the reflected way. This allows having a straightforward and accurate measurement of the Amplitude Ratio and Differential Carrier Phase between the direct and reflected signals. The possible applications are precise carrier phase altimetry and any application requiring signal amplitude ratio, or reflected signal Delay Map, with single or dual polarization, this include code altimetry, humidity, biomass, soil roughness, ocean surface wind and wave height, and snow and ice characteristics retrieval. This software is intended to be used as a research tool. It has been tested for carrier phase altimetry on real data sets collected in rather calm water conditions: at the 60-m Cordouan Lighthouse, and during a 600- m high ATR42 flight over a lake. In both cases, continuous carrier phase measurement with a centimeter level precision was obtained when extending the CIT up to 500 ms. Increasing the CIT beyond 20 ms is the key to improve carrier phase altimetry robustness. Index Terms—GNSS, GNSS carrier phase altimetry, long coherent integration, reflectometry, software defined radio (SDR), software GNSS receiver.

GLONASS, BEIDOU, and GALILEO) currently in orbit and reflected on the Earth surface: oceans, continental waters (rivers, and lakes), and continental surfaces. The first application using such GNSS bi-static radar for scatterometry purposes was proposed by Hall and Cordy [1] in 1989. A few years later, Martin Neira proposed the PARIS concept to measure sea level [2]. Off-the-shelf GNSS receivers can be used for reflectometry, but with many limitations, so a receiver with specific signal processing has to be used in order to make efficient measurements of the reflected signal properties, and indeed several custom built hardware and software are documented. In the frame of our GNSS-R investigations, one of our main requirements is to have full control over the GNSS signal processing in order to be able to test innovative and efficient algorithms. For this purpose, the instrument architecture we are using consists in a GNSS signal recorder to collect raw GNSS signal sample and a GNSS software receiver with reflectometry signal processing, that will be used in differed time. In the future, with the ongoing improvement on computational efficiency, we expect the GNSS software processing to run in real time thus bypassing the need to record the signal. This paper describes the solution we have developed by adapting the open-source GNSS-SDR software [24] for GNSS-R processing and present the first results obtained from it.

I. INTRODUCTION/GNSS-R LOBAL Navigation Satellite System Reflectometry (GNSS-R) consist in recovering the electromagnetic signals emitted continuously by the GNSS satellites (GPS,

G

Manuscript received September 30, 2015; revised January 15, 2016 and April 15, 2016; accepted April 26, 2016. This work was supported in part by the Centre National d’´etudes Spatiales in the framework of the TOSCA project “Hydrologie, Oc´eanographie par R´eflectom´etrie GNSS (HORG)” and in part by the RTRA STAE foundation in the framework of the “Potentialit´es de la r´eflectom´etrie GNSS In Situ et Mobile (PRISM).” (Corresponding author: Laurent Lestarquit.) L. Lestarquit, G. Wautelet, and R. Biancale are with the Centre National d’´etudes Spatiales, Toulouse 31401, France (e-mail: [email protected]; [email protected]; [email protected]). M. Peyrezabes was with the Centre National d’´etudes Spatiales, Toulouse 31401, France. He is now with the M3System, Lavernose 31410, France (e-mail: [email protected]). J. Darozes, N. Roussel, G. Ramilien, and F. Frappart are with the G´eosciences Environnement Toulouse, UMR 5563, CNRS/IRD/UPS, OMP, Toulouse 31400, France (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). E. Motte and M. Zribi are with CESBIO, UMR 5126, Toulouse 31400, France (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2016.2568742

II. STATE OF THE ART/PUBLISHED RESEARCH A. Existing GNSS-R Solutions When it comes to making research on GNSS reflectometry, the first difficulty to overcome is having a capable receiver. Two options are possible. 1) Either use off-the-shelf equipment, which is available GNSS receiver hardware. But the range of possible processing is essentially limited to either differentiate the observable from two standard GNSS receivers connected to the direct and reflected antenna, hoping there is a strong enough specular reflection to track the reflected signal [4], or using the Interference Pattern Technique (IPT) [3], [4], [11], based on measuring the frequency of C/No oscillation caused by the reflecting signal interfering with the direct one. 2) Use a custom made equipment with state-of-the-art joined direct and reflected signal processing. There are two families of processing according to [14]: a) The classical GNSS-R (cGNSS-R) which consists in correlating the direct and reflected signal with clean replicas generated by the receiver.

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b) The interferometric GNSS-R (iGNSS-R) which consists in direct cross-correlation of the direct signal against the reflected signal. When applied to CDMA signals, this technique requires to have a beam-forming antenna to isolate the GNSS satellites, such as in the PARIS project. An omnidirectional antenna can be used with GLONASS because its legacy signals are FDMA [17] therefore each visible satellite have a different frequency, but it will be so only for a few more years as they will be eventually replaced by modernized CDMA signals in the new GLONASS-K satellites. As we plan to use hemispherical antennas for our applications, devoid of satellite pointing capabilities, and with GPS and GALILEO constellations, the cGNSS-R is the desired technique for our software. Several research teams have developed their in-house receiver based on either HW or SW solutions, here is a list of the more relevant documented instruments. 1) The GOLD-RTR [7] from IEEC-ICE, this L1 hardware receiver can correlate signals from 10 different satellites in each correlation channel to obtain a 64-lag Delay Map (DM) or one signal from one satellite to obtain a 640bin Delay Doppler Map (DDM). The instrument provides complex waveforms, every millisecond, and has three radio front-ends for receiving signals from up to three antennas. 2) The GORS instrument [8] from GFZ, originally developed for occultation and based on a modified Javad receiver with the reflected processing in a master/slave configuration that outputs complex I/Q correlation results at a 200 Hz rate. An interesting point is this instrument supports L1/L2 GPS signals as well as E1 GALILEO signal. 3) Starlab has also published about a similar single frequency (L1) instrument, the PARFAIT instrument [9]. More recently, they have developed the SAM sensor with the signal processing done in a software receiver that consist in a “pair of twin GNSS receivers working in a master-slave configuration” [12], a signal processing that could be close to our desired solution. Starlab also sells the OCEANPAL instrument, predominantly devoted to altimetry. 4) In [20], the University of Colorado and the NOAA have computed DDM with an instrument having the high level architecture we desire: signal recording and delayed software processing. The signal processing is based on the MATLAB GPS software from Borre et al. [21], but unfortunately there is too little information on the reflected signal processing. 5) In [10], the NICT (Japan) describes a real time receiver based on Software Defined Radio (SDR). Raw RF signal samples are collected using an off-the-shelf USRP from Ettus [27] to digitize the signal. There is no conventional signal tracking, instead, for computational efficiency, the signal samples are processed in 750 ms batches that are transformed in the frequency domain using FFT,

to compute the signal cross-spectra with the replica, then transformed back in the time domain to obtain the correlation functions. To enable this processing, the signal Doppler must be determined externally and removed in a first step, which could be a limitation for airborne uses. 6) In [17], the NICT and the Chalmers University describe a software SDR receiver that computes correlation function with the iGNSS-R processing using the same frequency domain approach as above applied to GLONASS signals collected with standard GNSS antenna. In this case, the iGNSS-R processing works because legacy GLONASS signals use FDMA. Most of these receivers are not available to the public and their processing is not fully documented. The Starlab equipment is available for sale, but with limited access to the low level observables and knowledge on the signal processing Although well documented, the last two software approaches do not allow for the signal processing we wanted. To perform the desired signal processing, we choose to modify the GNU-RADIO based, open source GNSS software receiver: “GNSS-SDR” developed by the Centre Tecnologic de Telecomunicacions de Catalunya [24]. The advantage of starting from this receiver is the direct signal processing is already fully implemented, including the signal acquisition, code generation, channel management, computer resource management (using the GNU-RADIO libraries), and position velocity and time (PVT) computation. As an open source software, its source code is freely downloadable at www.gnss-sdr.org, this software is used by the general GNSS community, there is an active user forum, and software updates and enhancements are frequently released. In particular, the processing of the modernized and new GNSS signals is progressively being implemented, including new GNSS signals with the data and pilot structure. And finally, being based on an open-source software, we can distribute the reflectometry add-on to the GNSS-R community. B. Considerations About Coherent Integration Time (CIT) For C/A code, in general GNSS processing as well as in cGNSS-R processing, the CIT is limited to the data message bit length (20 ms) unless the data message is wiped-off. This is so because a change of data bit sign during the CIT would otherwise destroy the correlation. We want to increase the CIT beyond 20 ms because we think this is the key to improve carrier phase altimetry performances and use domain. We have reviewed the published literature works to find how long is the CIT in existing experiments and instruments. The results are summarized in Table I. As you can see nothing has so far been published about extending the CIT beyond 20 ms for cGNSS-R based instruments, not to mention data wipe-off. And in the iGNSS-R family, for which data wipeoff is not an issue because the same data bit is present in both the direct and reflected signals that are multiplied together, we have found only the GLONASS-R software [17] in which the CIT is increased to 5 s.

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TABLE I SURVEY ABOUT THE CIT OF EXISTING INSTRUMENTS AND EXPERIMENTS, COMPILED FROM RELEVANT PUBLICATIONS Instrument/Institution name

Reference number

GOLD-RTR/CSIC-IEEC GORS/GFZ

[7], [28] [8], [19]

PARFAIT & SAM/Starlab

[9], [12]

SDR & GPU based/NICT GLONASS-R/ U. Chalmers U. Colorado/NOAA

[10]

PARIS IOD/ESA Crater Lake exp./JPL Aircraft experiment/JPL PYCARO/IEEC/UPC

CIT

Comments

10 ms unknown Carrier phase altimetry. No discussion about the CIT nor about data bit removal. 20 ms “The CIT was set to its maximum for a conventional GPS C/A code, 20 ms” unknown No discussion about the CIT

[17]

5s

[20]

1–5 ms

[29] [30] [31] [32]

1 ms 20 ms 10 ms 20 ms

iGNSS-R family For scatterometry applications, the CIT shall be small iGNSS-R family The carrier phase is used.

III. GNSS-R SOFTWARE SOLUTION A. Generic Master–Slave Processing for Classical GNSS Reflectometry (cGNSS-R) Our desired signal processing is represented in Fig. 1. The direct signal processing is the same than for a classical GNSS receiver, with a code and a carrier tracking loop in which three code replica (E: Early, P: Prompt, and L: Late) and two carrier replica (I: In-phase and Q: Quadrature) are generated and multiplied to the incoming signal to form six parallel correlation channels dumping their integration result periodically every k ms (with k an integer, usually ranging from 1 to 20), from which the code and carrier discriminator functions are built [16]. When the tracking loops are locked, the code Prompt replica and the carrier In-phase replica, generated from the NCO (Numerical Commanded Oscillator) are synchronized with respectively the incoming signal code and carrier. This allows retrieving code pseudorange and carrier phase measurements. The reflected signal is processed in a master–slave configuration by using exactly the same I and Q carrier replicas computed for the direct channel. The code replicas are generated by adding the estimated reflected signal delay to the direct code loop NCO. A similar processing is described in [19]. One of the specificities of our algorithm is that the navigation data sign is extracted from the direct signal In-phase and Prompt IDP correlator channel and multiplied to the reflected correlators output, so as to remove the sign inversions due to the data message. This process is known as data wipe-off [18] and, as a result, the CIT, which classically ranges between 1 ms and 20 ms for the C/A code, can be increased to much longer in the “Open loop processing” stage. The main advantages for this algorithm are as follows: 1) The robustness of the reflected signal processing due to the slaving to the direct signal tracking loop. The reflected signal is then processed in open loop, there is not any tracking loop that could lose lock.

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2) The reflected signal complex correlator outputs give a direct measurement of the Carrier Phase Difference (CPD) between the direct and reflected signals. Indeed, on a phasor diagram (see Fig. 2), the direct signal carrier phase is aligned with the I-axis, and the angle between the I-axis and the reflected signal phasor is the CPD between the direct signal (antenna A) and the reflected signal (antenna B) due to the additional path of the reflected ray (see Fig. 3). The measurement is ambiguous by an integer number of carrier wavelength (19.03 cm for GPS L1). This can be used for carrier phase altimetry. The code delay applied in the processing is determined using the following equation: ρAB = (2h − d) · sin ()

(1)

where h, the direct antenna height, is computed using the antenna position obtained from the standard GNSS direct signal processing and an approximate Digital Elevation Model (DEM) or the reflecting surface altitude, the reflected antenna is located at the known distance d below the direct antenna, the satellite elevation  is computed from the collected satellite ephemeris message. Only a rough estimate of the reflected signal delay is needed, with an accuracy of only a fraction of the chip length. A suitable value could be 1/10th of a chip for which ∼90% of the useful signal amplitude is recovered, and corresponds to a ∼30 m accuracy for the C/A code. It could be a higher fraction, but at the expense of losing valuable useful signal power. The DEM for ocean level can be approximated to a simple one, e.g., the average sea level w.r.t. the WGS84 height reference used in the GPS system. For lakes, we use the a priori known elevation from topographic map. In case the antenna height cannot be determined with enough precision, for example for a lake with high water elevation changes, or for land with sloped terrain, it is possible to use N code replica instead of 1 and find the one with the higher correlation power from which to extract the measurements. In the best case, only one code replica is needed for the reflected signal processing, that is an additional two correlators (I&Q) on top of the six correlators already used in the direct signal processing, this results in a moderate increase in computational load. If the number of code replica is increased, due to uncertainty in the code delay to apply, or for applications requiring several code delays, so is the computational time load. The advantage of our solution is the possibility to have a userdefined number of correlators in the configuration file of the GNSS-SDR reflectometry add-on. B. Usage Domain for This Software Architecture This signal processing architecture is usable for any applications in which the following two conditions are met. i) The Doppler difference between the direct and reflected signal (Δ DR −D ) is lower than 500 Hz. This insures the correlation losses (CL) due to the Doppler mismatch according to (2) are

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Fig. 1. Signal processing architecture. The same carrier replica is used for direct and reflected carrier wipe-off. PVT is Position Velocity, Time, PPP is Precise Point Positioning, and NTM is Numerical Terrain Model.

ii) The reflected minus direct path length difference ρAB can be modeled to within several tens or a few hundreds of meters in order to determine the code delay offset. Condition (i) is met for any receiver placed on the ground, on board an aircraft or a balloon up to stratospheric altitude. Indeed, at the GPS L1 frequency (19 cm wavelength), this Doppler offset corresponds to a reflected minus direct path length increase of up to ∼100 m/s. Taking the first derivative of (1): ρ˙ AB = (2h − d) · ˙ · cos  + 2h˙ sin . Fig. 2. Complex correlator output represented on a phasor diagram. By construction of the signal processing, the direct signal is located on the I-axis positive side. In the ideal case the reflected signal power is constant, it should be located on a circle centered on the origin. The angle between the I-axis and the reflected signals gives the reflected minus direct signal CPM.

lower than 4 dB. Indeed, correlations have a CIT Tci of 1 ms  CL (ΔDR −D , Tci ) =

sin(π · ΔDR −D · Tci ) π · ΔDR −D · Tci

2 .

(2)

(3)

The first term is the contribution of the GNSS satellite elevation change , ˙ bound by 30° per hour and proportional to the height and is therefore lower than 10 m/s for a stratospheric balloon flying at a 40-km altitude, the second term is the height change rate, which is usually well below the 50 m/s limit permitted here even for an airplane climbing or descending. Condition (ii) requires having a rough terrain model to know the specular reflection point altitude. The receiver antenna position can be determined from the direct GNSS signal. For altimetry over ocean, lake or river, all that is needed is a water level estimate.

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where IR and QR are the correlator outputs for the reflected signal, IDP and QDP are the direct signal prompt replica. For the amplitude ratio to make sense, both the direct and reflected RF chain gain shall be either calibrated or estimated. 4) Applications that require both LHCP and RHCP polarizations can also be processed by adding an additional reflected signal channel, which has been implemented as an option. 5) DMs, also known as complex waveforms of the reflected signal can be produced when placing N code replica instead of one. 6) And finally complex DDM can be computed from the 1 kHz correlations outputs with the N delayed replica, but with the limitation in Doppler offset to around ±500 Hz using the following formula:

Fig. 3.

DDM(τn , ΔD, tk ) =

Geometrical setup for dual antenna GNSS-R altimetry.

+L  1 (IR n (k + l) CL (ΔD, Tc i ) l = 1 −L

+ jQR n (k + l)) × e−2 π j ·Δ D ·l ·T i s (6)

To sum up, the only use case not covered by this signal processing architecture is space-borne reflectometry due to the high Doppler difference. It would require correlating the reflected signal with a carrier shifted in frequency such as in the GOLD-RTR instrument [7]. All the other use cases are covered: receivers placed near the ground, or on board aircrafts or balloon up to stratospheric altitude. C. GNSS Reflectometry Applications Covered by This Signal Processing Scheme With the reflected waveform correlator outputs at 1 ms rate, the following can be computed (“open-loop processing” box in Fig. 1). 1) First of all and as a preamble to further processing, the reflected signal I&Q output can be coherently summed over the desired length of time. Thanks to the data bit removal, there is no 20-ms limitation for coherent integration. Long CIT is highly effective at filtering noise or removing non-coherent reflection components. It can be performed over several tens of seconds [5] if needed and if the signal coherence time allows this. 2) The CPD between direct and reflected signal ΔφR −D is the angle between the I-axis and the reflected signal correlator output IR and QR placed on the phasor diagram (see Fig. 2) and is computed from (4) where “arctan2” is the fourth-quadrant arctangent function and “unwrap” is the function that unwraps the carrier phase by correcting gaps higher than half a carrier cycle ΔφR −D (t) = unwrap (arctan2(QR , IR )) .

(4)

3) The amplitude ratio between the reflected and direct signal ΔAR −D can be computed with (5):  I 2 + Q2R ΔAR −D (t) =  2 R (5) IDP + Q2DP

where τn is the nth reflected code replica delay w.r.t. the direct signal, ΔD is the Doppler offset from the direct signal, tk is the time at which the DDM computation is centered, IRn (i) and QRn (i) are the complex correlator outputs for the nth reflected code replica computed at time ti , Tis is the inter sample time, equal in this case to the CIT Tis (1 ms), and 2L × Tis . is the time span over which the DDM is coherently integrated. Note that the DDM resolution in Doppler cannot be better than (1/2L · Tis ). A correction of the CL due to the Doppler offset is applied. 7) Once the signal amplitude and/or phase have been computed, for a single code delay, for a DM or for a DDM, further noncoherent summation is possible to reduce measurement noise 8) To obtain smoother results, integration over a moving window can be computed instead of performing classical coherent or non-coherent integration. It can be a classical rectangular window or a more elaborate apodization window such as a Hamming window, efficient at removing high frequency measurement noise. These open-loop processing cover the following applications: carrier phase and/or code range (DM) altimetry, Surface humidity and biomass retrieval using the relative signal amplitude, surface roughness, wave height or surface winds using the DM or the DDM [20], and finally snow, ice or low elevation applications in which RHCP polarization can dominate below the Brewster angle [23] with the dual polarization processing option. In fact, the only reflectometry application not covered by this signal processing architecture are those requiring DDM with a Doppler offset greater than 500 Hz, that is for example target detection from an aircraft.

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TABLE II GNSS-SDR V0.0.6 WITH REFLECTOMETRY ADD-ON EXECUTION TIME TO PROCESS A 60 S RECORDED FILE ON A DESKTOP COMPUTER WITH 4 INTEL CORE I3 @ 3.40 GHZ Number of satellites channels 5 5 10 10 10 10 10

Sampling rate (MSamples /s)

Number of reflected correlator pairs

LHCP only or RHCP+LHCP

Execution time (s)

4 8 4 8 4 4 4

1 1 1 1 10 20 1

LHCP LHCP LHCP LHCP LHCP LHCP RHCP+LHCP

77.0 99.7 110.1 163.9 135.9 194.5 156.0

The built-in resampling tool was used and is included in the processing time.

D. Signal Processing Using GNSS-SDR We implemented the GNSS reflectometry function in the version 0.0.5 of GNSS-SDR as an add-on that can be activated through a configuration file. In September 2015, version 0.0.6 was released and we have just migrated to this later version. Version 0.0.6 was upgraded with GPS L2C signal processing, which opens the way for dual frequency processing. It was also upgraded with CUDA libraries to be used with NVIDIA GPU cards. In the recent past, GNSS software receivers were considered to be slow, but with the progress in computational power, having a reflectometry real time processing with GNSS-SDR is close to becoming a reality. Table II presents the processing times for a 60 s input file processed with a desktop computer powered by 4 Intel Core i3 clocked @ 3.40 GHz. For the lightest configuration (five GPS satellites, 4 MSamples/s, and one pair of correlators on the reflected way), the processing time is 77 s, that is close to real-time. The input data were 2 bits I&Q complex data. Using a GPU card would allow an even faster execution, using multiple parallelization pipelines. We note that the addition of 20 reflected correlators to produce DM does not even double the processing time. The outputs given by the GNSS-SDR reflectometry function are the reflected signal I&Q complex correlators output at a user defined rate (1 kHz or less), from which further processing described in Section III-C is done. E. GNSS Front-End The GNSS-SDR software is compatible with most of the existing GNSS front-ends or signal recorders. Indeed, the front-end data formats (number of bits, complex I&Q or real sampling, sampling rate, and IF frequency) are set in the configuration file, and built-in conversion and/or resampling tools can be activated if needed. Several examples of configuration files are provided with the GNSS-SDR software package, for example for the popular Ettus USRP [27] front end. In our experiment, we build the configuration files according to the front-ends we have used.

IV. RESULTS A. Carrier Phase Reflectometry Versus Interference Pattern Technique (IPT) One of the primary objective for which this software receiver was developed is carrier phase altimetry. Carrier phase measurements are much more precise than code measurements, but can be impaired by loss of phase coherence in case of a rough reflection surface [14]. Altimetry using the IPT was applied by GET at the 60-m Cordouan light house [15]. The main advantages of IPT is it can be put into practice with off-the-shelf GNSS receivers and its robustness under rough sea conditions [4], its drawbacks are the low antenna gain towards the ground, and the delay of the reflected path that shall not exceed the chip length, that is, 293 m for C/A code, but only 29.3 m for the L5 signals [6]. The reason the IPT technique works so well despite having a limited antenna gain towards the ground is because it is an indirect method for making CPD measurements. With the software we have developed, we now have a direct method to make CPD measurement, at signal processing level and with a high temporal resolution. B. Cordouan Lighthouse Trial 1) Setup: The Cordouan lighthouse is located in the Gironde estuary in south west France, on the Atlantic ocean. It is a 60-m high tower that is fully surrendered by agitated ocean water at high tide. At low tide, there are numerous sand bars and rocky outcrops in the surroundings that make the water calmer. The lighthouse is only accessible during low tide and as we conducted a short trial of the Syntony Echo-L GNSS signal recorder [13], the measurements were made at low tide with 30 s long recording sessions of L1 signals at with 8-bits I&Q recording at 25 Msamples/s, which is a bit luxurious, half this sampling depth would not degrade the signal quality and shall speed-up the processing. The results we will show suggest that there is an interest in making longer recording sessions next time. We used the antenna setup of the OCEANPAL instrument (from Starlab Barcelona) which was installed there. Both antenna where tilted at a 20° angle (see Fig. 4), so as to increase the antenna gain towards the reflection point. The drawback was a poor isolation at antenna level between direct and reflected signals for low elevation satellites. 2) Results: We show the detailed results for PRN 15 as an example (see Fig. 5). The raw 1 ms correlator output phasor diagram is very noisy. When applying coherent integration over a sliding 500 ms Hamming window we obtain much better results (see Fig. 6), in which we see the I-axis is well aligned with the direct signal (except for a very short period which corresponds to the initial convergence after signal acquisition), and the reflected signal describes two circles around the origin but with significant amplitude and phase scintillation. The reflected signal power is almost as high as the direct one which is unusual and subject to caution as no power calibration of the RF chains or antenna gain has been done and there is suspicion the reflected antenna LNA could have a higher gain than the direct antenna one.

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Fig. 4. (a) Cordouan lighthouse (45°35 11 N; 1°10 24 W) culminates 60 m above the sea level. (b) It is located at the mouth of the Gironde estuary in the South West of France. (c) Both antenna are tilted to increase the gain toward the signal direction of arrival.

Fig. 7. CPD between reflected and direct signal over time. The unit is L1 carrier cycle (19 cm). Each satellite has been shifted by 1 cycle for clarity and ordered according to path length change. The red curve is the measured CPD, the blue curve is the modeled evolution for a 60 m antenna height.

Fig. 5. PRN15 correlator outputs at 1 ms shown on a phasor diagram. There is significant noise even on the direct signal.

Fig. 6. PRN15 phasor diagram showing the correlators outputs coherently integrated over a 500 ms Hamming window.

Fig. 7 shows the CPD measurement for every satellite in view. For PRN 15, as expected, there is a two cycle decrease over 30 s in the measured CPD. For all satellite we observe phase scintillations at roughly 1 Hz. We modeled the expected CPD variation due to the GNSS satellite elevation change. The measurements are ambiguous by an integer number of carrier wavelength and we shifted each satellite by one carrier cycle for clarity. PRN 15, 17, 24 and 25 delay changes match with the model at the centimeter level which is outstanding. Be aware that such good results would not have been possible if the CIT had been limited to 20 ms as this is the case for most GNSS-R application (see Table I). For PRN 12, we observe increasing phase scintillation leading to a cycle slip at t = 25 s. It is explained by reflected signal power fading on the phasor diagram. PRN 12 has a high elevation (65°) and its reflection point is located close to the foot of the lighthouse, in an area where, at low tides, there are many rocky outcrops, this might explain the signal fading. 3) Perturbation due to the Direct Signal Entering the Reflected Antenna and Mitigation: When looking more closely at the PRN 25 phasor diagram in Fig. 8, we realize the reflected signal describes a circle slightly offset from the origin. In addition to this, we notice a small oscillation of the measured CPD relative to the model. The explanation is there is a direct signal component entering the reflected signal antenna due to the antenna tilting and the fact PRN 25 is a rather low elevation satellite (27°). The direct signal entering the reflected antenna is not aligned with the direct signal entering the direct antenna because the antenna phase centers are separated by distance d. If we remove this direct signal component (modeling it as

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Fig. 8. PRN 25 phasor diagram showing there is direct signal (green arrow) received by the reflected antenna.

Fig. 9. PRN 25 CPD measurement with direct signal component removed from the reflected antenna.

the vector between the origin and the best estimation for the reflected signal circle center), the measured CPD now matches exactly the model (see Fig. 9). For PRN 26, the measured CPD does not match the model and there seems to be a cycle slip around t = 22 s. PRN 26 is a very low elevation satellite (5°) with an azimuth close to the antenna pointing direction. Therefore we expect a high level of direct signal entering the reflected antenna, and vice-versa. Indeed, the reflected signal “circle” is totally offset from the origin with a direct signal component almost as strong as the reflected signal (see Fig. 10). On the phasor diagram, the reflected correlator output circles two-time around. The first circle loops around the origin, but the second one does not, this explains the cycle slip. When removing the estimated direct signal component, we obtain a CPD measurement compliant with the model (Fig. 11), yet a bit nosier than for the other PRN. This is likely due to the reflection point location that is 600 m away, in an area with rougher water. We also notice a significant power fluctuation on the direct signal due to the reflected signal entering the direct antenna (see Fig. 12). This is something expected as this is how the IPT works.

Fig. 10. PRN 26 phasor diagram. A strong direct signal (green arrow) enters the reflected antenna.

Fig. 11. PRN 26 CPD measurement when the direct signal is removed from the reflected antenna.

To sum up, it appears that having the antenna tilted results, for low elevation satellites, in the direct signal perturbing the reflected signal measurement, and conversely. This can be mitigated with modeling of the reflected signal phasor perturbation, or by having both antennas in a horizontal setting. C. ATR42 Flight Campaign We had the opportunity to test the GNSS-SDR reflectometry algorithm on aircraft collected data made available by the CESBIO laboratory. Data were recorded with the GLORI instrument [22] flying on board a SAFIRE ATR 42 [25]. The mission primary objective was biomass and soil humidity measurement. Data were recorded during a pass above the Biscarrosse Lake (see Fig. 13) located south west of Bordeaux. The ATR42 was flying at a 600-m altitude above lake level at a 95 m/s velocity in the middle of a calm night, perfect conditions for observing specular reflections from the lake. The aircraft was stabilized and flying leveled over the lake. The CESBIO instrument recorded single frequency GPS signal during 36 s. In order to compute the modeled phase variation, we had to determine the accurate airplane position from the single

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Fig. 12. PRN 26 correlator output for the direct signal. The Early, Prompt, and Late correlator envelopes are shown. The Early–Late chip spacing is ½. The power fluctuation is clearly visible.

Fig. 13. Location map of the Safire flight during the night of July 2015, with a zoom over the Biscarosse lake.

frequency L1 observables. We used double differencing with the RTKlib freeware package [26] and data from the nearby Mimizan (MIMZ) reference station from the French GNSS permanent network (known as RGP network). As 36 s of data was too short to have an accurate positioning, we used the RINEX data recorded by the single frequency aircraft GPS receiver that was also connected to the direct antenna. We correct tides effect, the tropospheric effect using the calculated Zenithal Delay, the ionospheric effect by using the IONEX TEC corrections and the precise ephemeris (SP3 from the IGS center). The obtained trajectory solutions of the aircraft are quite accurate with North RMS component of 2.92 cm, East one of 2.52 cm, and Up RMS component of 9.47 cm which decrease with the baseline length. The measured flight height is roughly constant at 625.7 ± 0.2 m and the trajectory is linear over the lake. We were able to observe up to nine reflected signals. Every GPS satellite produced a reflected signal. Due to the higher altitude, the reflected path length change due to GNSS satellite elevation change was 10 times higher than at the Cordouan lighthouse, as expected from (2). The aircraft motion added a small additional dynamic. For example, for PRN 6 we observed a fast wrapping of the reflected carrier phase with 30 cycles in 36 s (see Figs. 14 and 15).

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Fig. 14. PRN6 phasor diagram recorded during the Biscarrosse Lake overflight.

Fig. 15. PRN 6 CPD delay. The red curve is the observed delay, and the blue curve is the model.

We found a small (10–20 cm) drift between the model and the observation. We believe it is due to a de-synchronization of the recordings between the direct and the reflected RF chain. For this mission, the RF chains were only calibrated in power gain, but not accurately for synchronization and propagation delay as this was not needed for the GLORI primary mission devoted to moisture and biomass measurements. Yet, the raw CPD measurements showed very good behavior with centimeter precision. For the next campaign, a calibrated front-end that would allow making more accurate measurements will be used. V. CONCLUSION We have successfully added a state-of-the-art GNSS reflectometry signal processing to the GNSS-SDR software, making it a useful research tool for GNSS reflectometry. The release of the add-on to the reflectometry research community is envisioned. Its performances were demonstrated on two carrier phase altimetry experiments: the in situ Cordouan lighthouse sea level experiment using SYNTONY Echo-L GNSS signal recorder and on SAPHIRE ATR42 flight over the Biscarosse lake using CESBIO GLORI receiver. For both cases we used

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

a dual antenna setup: RHCP antenna for direct way and LHCP for the reflected one. The use of phase retrievals demonstrates the possibility to make accurate in situ and aerial altimetry over oceanic and continental waters. The advantages of our solution are 1) the signal processing simplicity and robustness, 2) the possibility to have large CIT beyond 20 ms which has demonstrated to be highly efficient for carrier phase altimetry, and 3) the full access to the correlator outputs which gives flexibility in term of applications and analysis. For example, we showed the possibility to identify signal leakage, at antenna level, on the direct and/or the reflected way. These mixed signals can lead to cycle slip that could be directly fixed by refocusing the I–Q circle, increasing the robustness and the accuracy of the measurements. ACKNOWLEDGMENT The authors would like to thank J. Korsakissok from Syntony for lending us the Echo-LP GNSS signal recorder used at the Cordouan lighthouse, N. Stiebing for the antenna installation at the Cordouan Lighthouse, the Cordouan Lighthouse keepers for the meals and the fishs, and the SAFIRE team to help us recording data with the CESBIO GLORI instrument during the SAFIRE ATR-42 flight over Biscarrosse Lake. They would also like to thank the RTRA STAE foundation (http://www.fondation-stae.net/en/actions/ projets-soutenus/?pg = 3) and the CNES. REFERENCES [1] C. Hall and R. Cordy, “Multistatic scatterometry,” presented at the IEEE Int. Geoscience Remote Sensing Symp., Edinburgh, Scotland, 1988. [2] M. Martin-Neira, “A passive reflectometry and interferometry system (PARIS): Application to ocean altimetry,” ESA J., vol. 17, pp. 331–355, 2015. [3] K. M. Larson, E. E. Small, E. Gutmann, A. Bilich, J. Braun, and V. Zavorotny, 2008, “Use of GPS receivers as a soil moisture network for water cycle studies,” Geophys. Res. Lett., vol. 35, Art. no. L24405, doi: 10.1029/2008GL036013. [4] J. L¨ofgren and R. Haas, “Sea level measurements using multi-frequency GPS and GLONASS observations,” EURASIP J. Adv. Signal Process., 2014. doi: 10.1186/1687-6180-2014-50. [5] L. Lestarquit, Y. Gregoire, and P. Thevenon, “Characterizing the GNSS correlation function using a high gain antenna and long coherent integration— Application to signal quality monitoring,” in Proc. IEEE/ION Position Location Navigat. Symp., Myrtle Beach, SC, USA, pp. 877–885, Apr. 2012. [6] N. Rodriguez-Alvarez et al., “Soil moisture retrieval using GNSS-R techniques: Experimental results over a bare soil field,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3616–3624, Nov. 2009. [7] O. Nogu´es-Correig, E. Cardellach Gal´ı, J. Sanz Campderr`os, and A. Rius, “A GPS-reflections receiver that computes Doppler/delay maps in real time,” IEEE Trans. Geosci. Remote Sens., vol. 45, pp. 156–174, Jan. 2007. [8] A. Helm et al., “GORS—A GNSS occultation, reflectometry and scatterometry space receiver,” in Proc. 20th Int. Techn. Meet. Satellite Div. Inst. Navig., Fort Worth, TX, USA, Sep. 2007, pp. 2011–2021. [9] M. Caparrini, L. Ruffini, and G. Ruffini, “PARFAIT: GNSS-R coastal altimetry,” presented at the Workshop Oceanography GNSS Reflections, Barcelona, Spain, 2003. [10] T. Hobiger, J. Amagai, M. Aida, and H. Narita, “A real-time GNSS-R system based on software-defined radio and graphics processing units,” Adv. Space Res., vol. 49, no. 7, pp. 1180–1190, 2012. [11] N. Roussel et al., “Detection of soil moisture variations using GPS and GLONASS SNR data for elevation angles ranging from 2° to 70°,” IEEE J. Sel. Topics Earth Obs. Remote Sens., 2016, doi: 10.1109/ JSTARS.2016.2537847. [12] A. Egido, “GNSS reflectometry for land remote sensing applications,” Ph.D. dissertation, Univ. Polit`ecnica de Catalunya, Barcelona, Spain, May 2013.

[13] [Online]. Available: http://www.syntony.fr/index.php/record-replay, 2016. [14] V. Zavorotny, S. Gleason, E. Cardellach, and A. Camps, “Tutorial on remote sensing using GNSS bistatic radar of opportunity,” IEEE Geosci. Remote Sens. Mag., vol. 2, no. 4, Dec. 2014, pp. 8–45. [15] N. Roussel et al., “A GNSS-based alternative to classical tide-gauge to estimate sea-level variations,” Remote Sens. Environ., vol. 171, pp. 261– 277, 2015, doi: 10.1016/j.rse.2015.10.011. [16] E. Kaplan and C. Hegarty, Understanding GPS: Principles and Applications. 2nd ed. London: U.K.: Artech House, 2006. [17] T. Hobiger, R. Haas, and J. S. L¨ofgren, “GLONASS-R: GNSS reflectometry with a frequency division multiple access-based satellite navigation system,” Radio Sci., vol. 49, pp. 271–282, 2014, doi: 10.1002/2013RS005359. [18] T. Reng, M. Petovello, and C. Basnayake, “Improving GNSS bit synchronization and decoding using vector tracking,” presented at the ION GNSS+ Conf., Nashville, TN, USA, 2013. [19] M. Semmling et al., “A zeppelin experiment to study airborne altimetry using specular global navigation satellite system reflections,” Radio Sci., vol. 48, no. 4, pp. 427–440, 2013. [20] N. Rodriguez-Alvarez, D. Akos, V. Zavorotny, J. Smith, A. Camps, and C. Fairall, “Airborne GNSS-R wind retrievals using delay-Doppler maps,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, Jan. 2013, pp. 626–641. [21] K. Borre, D. M. Akos, N. Bertelsen, P. Rinder, and S. H. Jensen,“ A software-defined GPS and Galileo receiver: A single-frequency approach,” in Applied and Numerical Harmonic Analysis. Boston, MA, USA: Birkh¨auser, 2007. [22] E Motte, P. Fanise, and M. Zribi, “GLORI (GLObal navigation satellite system Reflectometry Instrument),” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2015, pp. 4773–4776. [23] S. Jin, E. Cardellach, and X. Feiqin, GNSS Remote Sensing: Theory, Methods and Applications. Berlin, Germany: Springer, p. 276. [24] C. Fern´andez-Prades, J. Arribas, P. Closas, C. Avil´es, and L. Esteve, “GNSS-SDR: An open source tool for researchers and developers,” presented at the ION GNSS Conf., Portland, Oregon, Sep. 19–23, 2011. [25] [Online]. Available: http://www.safire.fr/web/index.php?lang=en, 2016. [26] N. Kubo, T. Takasu, and A. Yasuda, “Development, evaluation and application of RTKLIB: A program library for RTK-GPS,” presented at the Int. GPS/GNSS Symp., Tokyo, Japan, Nov. 20–22, 2007. [27] [Online]. Available: http://www.ettus.com/product, 2016. [28] E. Cardellach, F. Fabra, O. Nogu´es-Correig, S. Oliveras, S. Rib´o, and A. Rius, “GNSS-R ground-based and airborne campaigns for ocean, land, ice, and snow techniques: Application to the GOLD-RTR data sets,” Radio Sci., vol. 46, 2011, Art. no. RS0C04, doi: 10.1029/2011RS004683. [29] A. Rius et al., “PARIS interferometric technique proof of concept: Sea surface altimetry measurements,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., Munich, Germany, 2012, pp. 7067–7070. [30] R. Treuhaft et al., “2-cm GPS altimetry over Crater Lake,” Geophys. Res. Lett., vol. 28, no. 23, pp. 4343–4346, 2001. [31] S. T. Lowe, C. Zuffada, Y. Chao, P. Kroger, L. E. Young, and J. L. LaBrecque, “5-cm-Precision aircraft ocean altimetry using GPS reflections,” Geophys. Res. Lett., vol. 29, no. 10, pp. 13-1–13-4, 2002, doi: 10.1029/2002GL014759. [32] H. Carreno-Luengo and A. Camps, “Empirical results of a surface-level GNSS-R experiment in a wave channel,” Remote Sens., vol. 7, pp. 7471– 7493, 2015.

Laurent Lestarquit received the engineering diploma from the Ecole Polytechnique, Paris, France, in 1994 and the Diploma degree in space engineering from Supaero (now ISAE), Toulouse, France, in 1996. He is a GNSS Expert in the CNES/GRGS space geodesy team and IGS analysis center hosted in the GET laboratory, Toulouse, France. His previous activities at CNES include GNSS signal design (with the Galileo Signal Task Force), signal processing, and spaceborne GNSS receivers. Mr. Lestarquit received the European Patent Office special price within the 2013 European Satellite Navigation Competition for the invention of the constant envelope Alt-Boc modulation and the CBOC waveform that are used on GALILEO.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. LESTARQUIT et al.: REFLECTOMETRY WITH AN OPEN-SOURCE SOFTWARE GNSS RECEIVER: USE CASE WITH CARRIER PHASE ALTIMETRY

Mathieu Peyrezabes received the Diploma degree in physics and more precisely in signal processing from the University of Bordeaux, Bordeaux, France. During the graduation internship, he worked on an innovative algorithm of reflectometry. He implemented this method in an open-source software developed in C++ on behalf of the French Space Agency (Centre National d’Etudes Spatiales), Toulouse, France. He is currently working as a Study Engineer in radionavigation with M3Systems, an independent SME specialized in geolocalization in Toulouse. Jos´e Darrozes received the Graduate degree from the Montpellier II University, France in 1997. His Ph.D. research focused primarily on remote sensing and wavelet signal processing in Earth Sciences. Since 1998, he has been working as an Associate Professor at the University Paul Sabatier, Toulouse, France and since 2011, he has been involved in the reflectometry activities. He is currently working with the reflectometry group hosted in the GET laboratory, Toulouse, France. Erwan Motte received the Graduate degree in microwave and optical communication systems from the French Telecom Lille 1, Villeneuve-d’Ascq, France, in 2002. He then focused on the use of these technologies in the frame of the international Master’s program “Radio and Space Science: Astrophysics – Earth observations – Technology” at the Chalmers University, Gothenburg, Sweden, where he received the Master’s degree in 2004. From 2004 to 2008, he worked, on a microwave radiometer for the study of water vapor on the middle atmosphere in the frame of the Ph.D. degree, funded by the French Space Agency (CNES) and the National French Center for Research (CNRS). In January 2009, he joined Starlab to work on space, microwave, and GNSS-R related projects. He is currently at the Centre d’Etudes Spatiales de la BIOsph`ere, Toulouse, France, working on airborne GNSS-R remote sensing for land applications.

Nicolas Roussel received the B.S. and M.S. degrees in topography and land surveying from the Institut National des Sciences Appliqu´ees, Strasbourg, France, in 2012. He received the Ph.D. degree in Geodesy and remote sensing from the Universit´e Paul Sabatier, Toulouse, France, in November 2015. In January 2016, he joined the MISTRALE project funded by the European GNSS Agency under the European Union’s Horizon 2020 research and innovation program. His research interests include GNSSreflectometry, altimetry, and remote sensing.

Gilles Wautelet obtained the Ph.D. degree which covered the study of the terrestrial ionosphere and its impact on precise positioning with GNSS from University of Li´ege, Belgium in 2013. He is currently working as a Postdoctoral Fellow with the space geodesy team at the Centre National d’Etudes Spatiales, Toulouse, France. His research interests include the characterization of multiGNSS biases in the frame of Precise Point Positioning with integer ambiguity resolution (iPPP).

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Fr´ed´eric Frappart received the Eng. degree in oceanography engineer from Ecole Nationale Sup´erieure des Techniques Avanc´ees Bretagne (ENSTA Bretagne, formerly ENSIETA), Brest, France, in 2001; and the Ph.D. degree in geophysics and remote sensing from the Universit´e de Toulouse, Toulouse, France, in 2006. He has been a Researcher with the Observatoire Midi-Pyr´en´ees(OMP), Toulouse, France, since 2010, in charge of the scientific applications of radar altimetry over land (hydrology and surface properties) for the Centre de Topographie des Ocans et de l’Hydrosph`ere (CTOH), a French Observation Service dedicated to radar altimetry studies, and member of the Scientific Definition Team of the NASA/CNES InSAR altimeter Surface Water and Ocean Topography (SWOT) mission for land hydrology (2012-2015) and of the SWOT Science Team (2016-2020). He is involved in GNSS-R activities in the Geodesy from space team at GET-OMP.

Guillaume L. Ramillien received the Ph.D. degree in space geophysics from the University Paul Sabatier, Toulouse, France, in January 1998, and the subject was the 3-D sea floor topography by inversion of radar satellite altimeter data. He held three successive postdoctoral fellowships (1998–2002) during which his main interest was the exploitation of remote sensing data for characterizing the Earth’s surface (hydrology) and subsurface geophysical processes. Since October 2002, he has been a Researcher at the Centre National de la Recherche Scientifique , Paris, France, and his current investigations are focused on the time variations of the Earth’s gravity field measured by the Gravity Recovery And Climate Experiment satellite mission, and he is interested in the preparation of the future low-altitude gravity missions. Since May 2015, he has been the President of the GRGS scientific council.

Richard Biancale received the civil engineering degree from the Paris High School of Civil Engineering, Paris, France, in 1975, the Master’s degree in oceanography, and the Ph.D. degree in astronomy from the Paris University, Paris, France, in 1978. In 1982, he joined Centre National d’Etudes Spatiales (CNES), Toulouse, France. He is the Chief of the Space Geodesy Team of the “Centre National d’Etudes Spatiales”, Toulouse, France, CNES Senior Expert and actual Executive Director of the “Groupe de Recherche de G´eod´esie Spatiale (GRGS)” as well as of the GRGS Scientific Council, Toulouse, France.

Mehrez Zribi received the engineering degree in signal processing from the Ecole Nationale Sup´erieure d’Ing´enieurs en Constructions A´eronautiques (ENSICA), Toulouse, France, and the Ph.D. degree in remote sensing from the Universit´e Paul Sabatier, Toulouse, France. In 1995, he joined the CETP laboratory (IPSL/CNRS), V´elizy, France. He is employed by CNRS (Centre National de Recherche Scientifique) since 2001. In October 2008, he joined CESBIO laboratory. His research interests include microwave remote sensing applied to hydrology and microwave modeling and instrumentations.