effects of the 2012-2013 solar maximum on gnss … · ibge, as the national mapping agency of...

GPS Solutions (2015) 19, 2, 309-319

GPS Solutions (The final publication is available at Springer via http://dx.doi.org/10.1007/s10291-‐014-‐0389-‐1)

EFFECTS OF THE 2012-2013 SOLAR MAXIMUM ON GNSSSIGNALS IN BRAZIL

Luiz Paulo Souto Fortes1,2 Tao Lin

Gérard Lachapelle

Position, Location and Navigation (PLAN) Group Department of Geomatics Engineering, University of Calgary 2500 University Dr. NW, Calgary, Alberta, Canada T2N 1N4

1: On leave from the Brazilian Institute of Geography and Statistics (IBGE) and from the University of the State of Rio de Janeiro (UERJ), as a Scholar of CNPq – Brazil

2: Corresponding author Email: [email protected]

Abstract

To investigate the effects of equatorial ionospheric scintillation on GNSS signal tracking performance during the current solar maximum a research project was established between the Brazilian Institute of Geography and Statistics (IBGE), the University of the State of Rio de Janeiro (UERJ), and the Position, Location and Navigation (PLAN) Group of the Department of Geomatics Engineering, University of Calgary (UofC). This was done using intermediate frequency (IF) data processed with a software receiver and code and carrier phase data obtained using a standard geodetic receiver located at the same site. The main benefit of using a software receiver is flexibility. IF data was collected daily from 8 to 12 pm local time (11 pm to 3 am UTC) using a dual frequency front-end at IBGE facilities in Rio de Janeiro, from June 2012 to March 2013, whenever scintillation occurred. Four observation sessions were selected for processing along with another one with no scintillation, for comparison. The front-end antenna was located only 5 m away from a standard receiver antenna of the Brazilian active geodetic network, to allow a comparison of the two solutions under the same conditions. In the observation domain, the software receiver produced no actual cycle slips and only up to 4% of missing L2 observations, indicating that the state-of-art acquisition and tracking algorithms used are resistant to equatorial ionospheric scintillation, whereas for the hardware receiver, up to 288 cycle slips were detected and 13% of L2 observations missing over a 4-hour session under strong scintillation. The latter represents an improvement of a factor of 3 over the performance of another hardware receiver used at the same site 10 years ago during the previous solar maximum. In the position domain, Precise Point Positioning solutions using L1 code observations gave very similar results for the software and hardware receivers in all sessions, with the ionosphere being responsible for degrading coordinate accuracies up to a factor of 12. The best positioning accuracies were those given by dual frequency solutions, at the centimeter to a few decimeters level in kinematic mode. Keywords

Solar maximum, equatorial ionospheric scintillation, GNSS signals, software receiver, observation and position domains

Introduction The solar maximum of the current Sunspot Cycle 24 was predicted to happen in 2013. As a result of the higher solar activity, ionospheric scintillation causing amplitude fading and phase fluctuation of the received GNSS signals is expected to happen more often and strongly. A research project was consequently established in 2012 to investigate the effects of equatorial ionospheric scintillation on GNSS signals tracking performance using a Software Navigation Receiver, namely GSNRx™ developed by the University of Calgary PLAN Group (Petovello et al. 2008). The time period of this study turned out to be appropriate, since the latest prediction shows that the solar maximum happened in October 2013 or later, with a previous peak in February 2012 (http://solarscience.msfc.nasa.gov/ predict.shtml). The main benefit of using a software receiver to study these effects is the flexibility of a software-based approach, since the same intermediate frequency (IF) data collected by a front-end can be used to process the data with an unlimited variety of signal acquisition and tracking algorithms.

IBGE, as the National Mapping Agency of Brazil, operates the Brazilian Network for Continuous Monitoring of GNSS (RBMC) (Fortes et al. 1998), an active geodetic network currently composed of about 100 continuous operating reference GNSS stations (CORS). The RBMC RIOD station is located at IBGE Coordination of Geodesy in Rio de Janeiro only five meters away from the site where IF data was collected for this research, making it possible to evaluate and compare the performance of the software receiver with that obtained with the RIOD’s hardware receiver, a Trimble NetRS at that time.

We first briefly revisit the ionospheric scintillation fundamental, particularly related to the equatorial region, and its amplitude index (S4) used in this study to assess the IF data collection conditions. The GSNRx™ main features are then described, as well as the data collection and data processing approaches. The assessment of signal tracking performance using GSNRx™ is performed based on results in the observation domain, through detecting dual frequency cycle slips and missing GPS L2 phase observations, and in the position domain by submitting the GSNRx™-derived observations to the Natural Resources Canada (NRCan) Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) Service. The results in both domains are compared with those obtained using the RIOD station observations as a reference to assess the performance of the software receiver. Equatorial ionospheric scintillation Ionospheric scintillations can be described as random rapid variations in the intensity and phase of the received signals resulting from electron density irregularities in the ionosphere. It happens more frequently in the polar, auroral and equatorial regions, especially around Solar Maximum periods, and in the Americas from September to March (Klobuchar 1996). The importance of scintillation for GNSS signal tracking is related to the ability of the receivers to maintain signal lock, particularly for GPS L2 signals. Given the 11 years that have elapsed since the previous Solar Maximum, it is necessary to assess the performance of signal tracking based on the current hardware and software receiver technologies.

The amplitude scintillation index (S4) and phase scintillation variance (σφ2 ) are commonly used to indicate the level of the scintillation activity. The S4 index is computed using the following equations (Van Dierendonck et al. 1993):

2

22

SI

SISI4S

−= (1)

where SI denotes the signal intensity and represents the average over usually a time interval of 60 s. SI is computed as follows:

SI = NBP – WBP (2) where NBP is the signal’s narrow-band power and WBP is the wide-band power, measured over the 60 s time interval every 20 ms according to the following:

220

1ii

220

1ii QINBP ⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛= ∑∑

== (3)

( )∑=

+=20

1i

2i

2i QIWBP (4)

where I and Q are the 1 kHz in-phase and quadraphase accumulation results of the channel correlators. The raw SI values computed by (2) are de-trended in this study by computing a moving average over 60 s and then normalized using the following equation:

madet SI

SISI = (5)

where SIma is the SI moving average. The final S4 values, after eliminating the effects of ambient noise, are then computed as (Van Dierendonck et al. 1993)

⎟⎟⎠

⎞⎜⎜⎝

⎛+−

−=

002

det

2det

2det

NC195001

NC100

SI

SISI4S (6)

where C/N0 is the carrier-to-noise ratio expressed in dB. S4 is a dimensionless number with a theoretical upper limit of 1.0. However, sometimes values above this limit can be found, most likely due to the filtering approach used in the de-trending process.

In the equatorial region the rapid phase changes, which are reflected on σφ2 , may be associated with signal fades, which are reflected on S4, as shown by Xu et al. (2012). Therefore, the S4 index is used in the present study to identify the occurrence of scintillation, with values above 0.3 and below 0.6 associated with weak scintillation and those above 0.6 with strong scintillation. GNSS Software Navigation Receiver - GSNRx™ Today, many GNSS receivers are capable of acquiring and tracking multi-GNSS signals; however, they usually process GNSS signals independently, except for tracking L2P(Y) signals with the semi-codeless technology. This is the so-called independent-channel architecture, which is simple and easy to use but it

ignores the correlation of the signals with multiple frequencies from the same and different satellites. We developed in GSNRx™ a shared-channel architecture, which utilizes the inter-channel aiding from channels and satellites for robust GNSS signal tracking under scintillation.

GSNRx™ is a C++ class-based GNSS navigation software receiver, which is capable of processing raw IF data samples in post-mission or real-time. More information on the initial version of GSNRx™ can be found in Petovello et al. (2008). Currently the software supports the following GNSS signals:

• GPS L1 C/A, L1C, L2C and L5 • GLONASS L1 and L2 • Galileo E1b/c, E5a and E5b • Beidou B1I and B2I

There are two main reasons to support the use of the proposed shared-channel architecture for

GNSS signal tracking under scintillation. First of all, the impact of scintillation on GNSS signals is carrier frequency dependent (Carrano et al. 2012). Thus, it is useful to utilize frequency diversity, such as aiding from L1 to L2, for signal tracking under scintillation. Secondly, the impact of scintillation is not the same for all satellites in view. Thus, utilizing satellite diversity such as using vector tracking loops is beneficial for signal tracking under scintillation.

The general software hierarchy of the modified GSNRx™ version used herein is shown in Fig. 1. The software architecture is primarily composed of a list of sample sources, satellites, channels, signals, and correlators, and a navigation solution. Each satellite contains one or more channels, each of which operates on one or more signals. A channel can contain a selection of correlation processing strategies which may include a variety of acquisition and tracking strategies for various signals. In this context, one channel is defined as the object of processing signals from one carrier frequency. If there is more than one signal in a channel, one is a master signal, and the other one(s) is a slave signal. The choice of the master signal depends on the existence of the data modulation and data bit period. Only the master signal needs to be tracked in one channel. For example, one L2C channel contains one L2CM signal, which is the master signal, and one L2CL signal, which is the slave signal. When processing signals from multiple frequencies, one channel in each satellite is selected as the master channel and others are slave channels. In this modified GSNRx™, master channels can be selected adaptively based on signal power, lock status, and code/frequency property, or manually based on the actual application. For signal acquisition, only acquiring the master signals in master channels are needed, since the code phase and carrier Doppler values of other signals can be obtained by the timing and carrier Doppler information from the master signals in master channels. For signal tracking, if carrier phase tracking is needed, the carrier Doppler aiding from the master channels can be used to initialize and aid the slave channels’ phase lock loops (PLLs) and detect the loss of carrier lock of the signals in the slave channels. Carrier-aided code tracking loops with an early-late (E-L) correlator spacing of 0.1 chip are used herein for all channels. The timing information derived from the master signals’ code phases in the master channels are only used to detect the loss of code locks of the signals in the slave channels and initialize their code tracking after loss of lock. Due to the benefit of the carrier Doppler adding from the master channels, the bandwidth of the PLLs in slave channels is reduced from 15 Hz to 5 Hz. This improves the tracking performance in the slave channels. Since the tracking performance in the slave channels is affected by the tracking performance in the master channels, improving the master channels’ tracking capability is necessary to improve the overall tracking performance. In this proposed shared-channel architecture, vector frequency lock loop aided PLLs (VFLL-PLLs) are adopted to track the master signals in master channels. The VFLL provides the carrier Doppler aiding to each master channel’s PLL based on the

navigation solution feedback; thus the master channel’s PLLs only need to track the residual carrier Doppler for carrier phase tracking with a narrower loop filter bandwidth.

For the data processing in this research, GSNRx™ was configured to process GPS L1 C/A and L2C signals because the front-end used in this research can only capture L1 C/A and L2C signals. In the software receiver, each satellite object contained two channel objects. The L1 C/A channels were configured as the master channels, while the L2C channels were the slave channels, because a) the transmitted power on L1 is stronger than the one on L2, b) the L1 signal is less sensitive to scintillation than L2C signal, because of the inverse relationship between scintillation parameters and signal frequency, and c) the C/A code is the shorter code compared to CM and CL codes; thus it is ideal for fast acquisition. In each L2C channel, L2CL was selected as the master signal.

Fig. 1 General hierarchy of the modified Software Navigation Receiver GSNRx™

Data collection In order to collect the IF data affected by equatorial scintillation, a 1-bit dual-frequency front-end developed in the PLAN Group by A. Morrison was installed at the facilities of IBGE Coordination of Geodesy in Rio de Janeiro (Fig. 2). The station, named RIOFE, is located under the Southern lobe of the equatorial anomaly’s daily path, where ionospheric scintillation generally peaks. Data was collected daily after sunset, from 20:00 to 24:00 local time, 23:00 to 3:00 Universal Time, from June 4, 2012 to March 29, 2013, whenever equatorial scintillation occurred. As it is difficult to predict the occurrence of equatorial scintillation and it would be practically impossible to collect and store data collected every day (the packed file size of each 4-hour session is about 67 GB), the actual occurrence of strong scintillation was verified by checking the S4 values measured by ionospheric receivers belonging to the Concept for Ionospheric Scintillation Mitigation for Professional GNSS in Latin America (CIGALA) network (Sreeja et al. 2011). Based on the S4 values measured by the closest operational CIGALA

station to Rio de Janeiro, SJCU at 350 km, the IF data collected the previous day at RIOFE was deleted or saved (Fig. 3). Additional information from the Low-Latitude Ionospheric Sensor Network (LISN) and from the Brazilian Space Agency Space Weather Study and Monitoring service (EMBRACE) was eventually verified in order to confirm the occurrence of scintillation. Besides the datasets collected under strong scintillation, a few others collected under quiet or moderate scintillation were also saved for comparison purposes. Following this approach, 79 session files were collected, totaling 5.16 TB (packed). Data analysis has shown a significant and consistent increase of S4 values from September 22, 2012, to March 20, 2013, i.e., between Equinoxes.

Fig. 2 Observation system on the roof of the IBGE building, Rio de Janeiro. (a) Antenna connected to the front-end; (b) RBMC RIOD station antenna located 5 m from the front-end’s antenna GSNRx™ processing From the IF data collection available, three sessions strongly affected by scintillation were selected for processing and analysis: October 24 and November 17, 2012, and February 20, 2013. For comparison, two more sessions were selected corresponding to a quiet scintillation period, from 22:10 to 2:10 local time on June 04-05, 2012, and to a session with fewer satellites affected by scintillation on March 28, 2013. Fig. 4 shows the S4 values above 10° measured by the CIGALA SJCU ionospheric receiver (a Septentrio PolarRxS ISM receiver) for all visible satellites during these five sessions.

Fig. 3 CIGALA Stations and the IBGE-UERJ-University of Calgary front-end station (Source: http://is-cigala-calibra.fct.unesp.br/is/cigala/index.php#)

After IF data processing by GSNRx™, the I and Q samples generated by the processing of the GPS L1 C/A signal were used to compute amplitude scintillation S4 values for some satellites using (1) to (6), confirming the level of scintillation measured at the CIGALA SJCU station during the five sessions selected for processing. Fig. 5 shows the computed S4 at RIOFE for some satellites during October 24, 2012 and the same information measured at SJCU. The different S4 values for individual satellites at RIOFE and SJCU may be explained by the spatial decorrelation of scintillation. I and Q samples generated by the GPS L2CM processing at RIOFE were also used to compute S4 for some satellites to illustrate the higher impacts of scintillation on the L2 (lower) frequency. Fig. 6 shows these results for PRN12 for October 24, 2012 along with S4 for the L1 C/A signal. It can be seen that S4 values are consistently higher for L2CM, in this case by an average factor of 1.2. Besides, these values show strong correlation with scattered C/N0 values for both L1 C/A and L2CM signals, as expected. Computing the average differences between L1 C/A and L2CM C/N0 values shown in Fig. 6 gives a figure of 5.3 dB-Hz, matching exactly the specification described by Fontana et al. in http://navcen.uscg.gov/pdf/gps/TheNewL2CivilSignal.pdf for the L2C data channel power relative to L1 C/A. Results in the observation domain The assessment of signal tracking performance under scintillation using the software receiver was performed in the observation domain through detecting dual frequency cycle slips and missing GPS L2 phase observations. These observations were generated by GSNRx™ runs with a rate of 2 Hz. The cycle slips detection approach used the following well known algorithm:

Fig. 4 S4 values measured by the CIGALA SJCU ionospheric receiver for all visible satellites above 10° elevation

Fig. 5 Computed S4 values at RIOFE for some satellites and measured at SJCU in October 24, 2012 session

Fig. 6 PRN12 S4 and C/N0 values for L1 C/A (light blue) and L2CM (dark blue) signals at RIOFE in October 24, 2012 session

( ) ( ) threshold12 t2L1Lt2L1L >Φ−Φ−Φ−Φ (7)

where ΦL1 and ΦL2 are the GPS L1 and L2 carrier phase observations, expressed in meters, at consecutive epochs t2 and t1 (based on the observation rate used, t2-t1 = 0.5 s); and is the absolute value operator. The threshold used to detect cycle slips was 0.24 m, equivalent to one L2 cycle. A limitation of this approach it that it is not possible to detect on which frequency the cycle slip occurs.

The same approach was applied to the GPS observations collected at the RIOD station. At the time, this station was equipped with a Trimble NetRS receiver with firmware version 1.2-0, installed in March 2008. For comparison purposes, the RIOD observations submitted to this analysis belong exactly to the same time window of the RIOFE sessions. As the observation interval used at RIOD is 15 s, a 7.2 m threshold, given by 0.24x15/0.5, was used to detect cycle slips at this station in order to be consistent with the threshold used at RIOFE and at the same time to avoid a false detection of cycle slips due a possible divergence between L1 and L2 due to the dispersive effect of the ionosphere.

The analysis herein also detected missing GPS L2 phase observations from both RIOFE and RIOD stations. GSNRx™ operates on L1 and L2C signals. Therefore, as the analysis in this session requires dual frequency data, only observations of satellites belonging to IIR-M and II-F constellations were used.

Table 1 lists the percentage of missing L2 observations and the number of detected cycle slips in each session at the RIOFE station. The same information for RIOD is also included. Only observations collected above 10° elevation were considered in this computation.

In Table 1, in addition to the results of individual satellites in each session, the total percentage of missing L2 observations and number of cycles slips for IIR-M and IIF satellites at RIOFE and RIOD are shown, as well as the same information for all visible satellites in case of RIOD, considering that the hardware receiver is capable of tracking the L2P(Y) signal from all satellites. For both stations, the detection of cycle slips was only performed when both L1 and L2 observations were present, thus the corresponding values do not reflect the cases when L2 was missing. Analyzing Table 1, the following comments can be made:

• In general, the tracking performance was very much correlated to the scintillation conditions present in each session, going from 0% and 2% (6% for all satellites) of missing L2 observations for the software and hardware receivers in the session with no scintillation (June 04-05, 2012) to 4% and 11% (13% for all satellites) in the session with the strongest scintillation (October 24, 2012); in terms of cycle slips, the range comes from 0 for both the software and the hardware receivers on June 04-05, 2012, to 0 and 182 (288 for all satellites) on October 24, 2012;

• The signal acquisition and tracking technology implemented in the GSNRx™ software receiver has shown an excellent L2 performance, with up to only 4% of missing observations under strong scintillation. Besides, it resulted in practically no cycle slips for all five sessions at RIOFE, even under strong scintillation. As the size of the only two cycle slips detected (PRN29 on November 17, 2012) was 0.25 m, thus slightly above the threshold (0.24 m), it may indicate that they are not actual cycle slips;

• The hardware receiver observations were strongly affected by cycle slips during scintillated days, with up to a total of 288 cycle slips in October 24, 2012 session and an overall 13% of missing L2 observations. All cycle slips detected at the RIOD station in all sessions were at the level of

hundreds or thousands of meters, confirming that they are actual cycle slips. In a day with no scintillation, there were no cycle slips, but still 6% of missing L2 observations;

• Analyzing the situation of individual satellites, those most affected by scintillation have more missing L2 observations and cycle slips, as expected, especially in the case of the hardware receiver. PRN15 on November 17, 2012 was the most affected satellite in terms of L2 tracking, with 25% of missing observations at RIOFE and 52% at RIOD.

Table 1 Percentages of missing L2 observations and number of detected cycle slips at RIOFE and RIOD stations

IIR-M/IIF Satellites (SV)

Missing L2 observations (%)

# of dual frequency Cycle Slips

RIOFE RIOD RIOFE RIOD Session: June 04-05, 2012

(no scintillation) 05 0 0 0 0 07 0 0 0 0 17 0 8 0 0

Total for IIR-M and IIF SV 0 2 0 0

Total for all visible SV - x - 6 - x - 0

Session: October 24, 2012 (strong scintillation)

05 16 17 0 22 12 0 5 0 47 15 2 20 0 23 25 0 1 0 3 29 1 18 0 60 31 3 22 0 27



Session: November 17, 2012 (strong scintillation)

05 1 4 0 19 12 5 4 0 38 15 25 52 0 0 25 0 0 0 5 29 0 0 2 0 31 0 0 0 0



Session: February 20, 2013 (strong scintillation)

01 0 5 0 2 31 7 8 0 2



March 28, 2013 (strong scintillation on fewer SV)

01 0 0 0 0 07 14 27 0 0 31 0 0 0 0



In order to assess the hardware receiver technology evolution since the previous solar maximum 11 years ago, observations collected by a Trimble 4000SSI receiver at RIOD from 20:00 to 24:00 local time on March 17, 2002 were submitted to the same quality check approach, i.e., detection of cycle slips using (7) and of missing GPS L2 phase observations. During that night, S4 over Brazil was mapped by Rezende et al. (2007), giving values up to 0.95 over Rio de Janeiro. Table 2 shows the percentage of missing L2 observations and number of dual frequency cycle slips detected for each visible satellite.

Table 2 Percentage of missing L2 observations and number of dual frequency cycle slips detected at RIOD on March 17, 2002

Satellites Missing L2

observations (%)

# of dual frequency Cycle Slips

04 37 4 05 32 12 06 36 0 07 2 0 09 53 15 10 49 2 15 59 3 17 23 5 23 20 19 24 62 14 26 39 4 28 49 0 29 85 2 30 23 9

TOTAL 41 89

Comparing the results listed in Table 2 with those of RIOD in Table 1 for the strongest scintillation case (October 24, 2012), it can be seen that tracking technology enhancement since the last solar maximum (from codeless to semi-codeless cross correlation) was responsible for a reduction of the percentage of L2 missing observations from 41% to 13%, i.e., by a factor of 3. On the other hand, the number of cycle slips increased from 89 to 288, which may be explained by the fact that more L2 observations were generated and then subject to cycle slips.

To assess the performance of a state-of-art hardware receiver when tracking data under the conditions of this research, observations collected by a Trimble NetR8 on October 24, 2012 at the RBMC UBA1 station, located 200 km from RIOD, during the same time span was also submitted to the quality check approach used in this section. The results showed 4% of missing L2 observations for IIR-M/IIF satellites and 7% considering all satellites. This performance is equivalent to that of the software receiver (4%) and better than that given by the RIOD hardware receiver (11 and 13%). In terms of cycle slips, there were 105 occurrences of them for IIR-M/IIF satellites and a total of 125 for all satellites, which is only slightly better than the figures given by the Trimble NetRS (182 and 288) (see Table 1). This means that the NetR8 receiver was also affected by scintillation on that day.

Results in the position domain In order to evaluate the impact of the ionosphere in the position domain, the GSNRx™-derived observations generated for the five sessions at RIOFE were submitted to the NRCan CSRS-PPP Service.

Analogously to what has been done in the observation domain, the observations collected at RIOD were also submitted to this service, for comparison. The observations generated by GSNRx™ at 2 Hz were decimated to 15 s to be consistent with RIOD before submission to the service. The observation files were submitted in kinematic mode to allow the necessary degree of freedom for the coordinates to be affected by the ionosphere through epoch by epoch estimation. PPP L1 code solutions were generated for all five sessions using RIOFE and RIOD data, providing 10 results in total composed by coordinates for each observation epoch in the corresponding session referred to ITRF(IGS08) (Rebischung et al. 2012). Fig. 7 shows the coordinate differences obtained for each observation epoch in each session.

The following remarks can be made based on Fig. 7:

• There is a strong correlation between the latitude, longitude and height differences obtained at RIOFE and RIOD in all sessions;

• When comparing the results with no scintillation with those under strong scintillation, it can be concluded that the large coordinate differences found in the latter in both stations, reaching values up to around 25 m in height on March 28, 2013, are mostly due to ionospheric delay caused by high Total Electron Content (TEC) values, whose variability can be associated to scintillation (Oron et al. 2013). Fig. 8 shows the TEC values for all visible satellites above 10° elevation at the CIGALA SJCU station during the same sessions. Comparing the plots in Fig. 8 with those in Fig. 4, the occurrence of high (and variable) TEC values during sessions with strong scintillation can be seen;

• The position results using the software receiver show a slightly higher L1 code noise when comparing with the hardware receiver. As explained previously, dual frequency observations (L1 and L2 carrier phase and code) are

currently produced by GSNRx™ for IIR-M and IIF satellites. CSRS-PPP requires a minimum of five satellites with all four observation types for at least one epoch in order to initialize an ionospheric-free processing. Once the solution is initialized, this requirement is reduced to four (Tétreault P 2013 Personal Communication). The satellites tracked on November 17, 2012 fulfilled these requirements for 1h 38min 45s, thus CSRS-PPP was able to generate a precise dual frequency PPP solution using this subset of GSNRx™ data. Fig. 9 shows the difference of coordinates for each observation epoch processed by dual frequency CSRS-PPP to the reference coordinates. The same type of solution using data collected at RIOD for all visible satellites during the entire four-hour session is also shown in the figure.

The results shown in Fig. 9 for RIOFE and RIOD are not quite comparable because the amount of observations processed is different for each case. The PPP solution for RIOFE was generated using observations of only four to five satellites per epoch for less than two hours, whereas the solution for RIOD was produced using observations for all visible satellites in each epoch for four hours, since the hardware receiver generates L2 code and carrier phase observations for all satellites. Nevertheless, the figure shows interesting aspects of both solutions which should be mentioned. In case of RIOFE, the convergence period of the PPP solution, necessary for the float ambiguities to converge to or close to integer values, can be seen with no discontinuities. The fact that GSNRx™ has not generated any cycle slip during the entire session surely contributed to it. On the other hand, the 146 cycles slips produced by the hardware receiver probably forced the processing to reset ambiguities more often in case of RIOD, causing the discontinuities shown in the plot. Further inspection of both PPP processing reports confirmed these assumptions.

Fig. 7 Latitude (blue), longitude (brown) and height (yellow) differences (in meters) of NRCan CSRS-PPP Service kinematic mode L1 code solutions for each observation epoch in each session relative to the reference coordinates at RIOFE and RIOD. Left panels refer to RIOFE station (IF Data) and the right panels to RIOD station (Trimble)

Fig. 8 TEC values (expressed in TECU units) measured by the CIGALA SJCU ionospheric receiver for all visible satellites above 10° elevation

Fig. 9 Latitude (blue), longitude (brown) and height (yellow) differences (in meters) of NRCan CSRS-PPP Service kinematic mode dual frequency solutions for each observation epoch in November 17, 2012 session relative to the reference coordinates at RIOFE and RIOD

The corresponding RMS and mean values of the coordinate differences shown in Fig. 7 and 9 for

all observation sessions at RIOFE and RIOD are listed in Table 3. Based on Table 3, the following comments can be listed:

• The RMS and mean values for all sessions are generally very similar for RIOFE and RIOD, confirming the strong correlation of the results in both stations seen in Fig. 7;

• Considering that the results for the session with no scintillation are dominated by L1 code noise and multipath, the values in the far right column for this session, computed in the PPP estimation process, confirm that both the software and hardware receivers generate L1 code observations at the sub-meter level (compatible with a narrow correlator technique), slightly higher in the case of the software receiver. For the other four sessions, with strong scintillation, the increased value in this column is caused by the effects of the ionosphere, with values between two and three meters;

• PPP L1 code position difference RMS values range from a sub-meter level (horizontal) and a meter level (vertical) in a session with no scintillation to a five-meter level (horizontal) and an eight-meter level (vertical) in a session with strong scintillation, i.e., a factor up to 12. As explained before, these large RMS values are due to ionospheric delay caused by high TEC;

• PPP L1 code position differences are biased by up to four (horizontal) and six (vertical) meters in sessions under strong scintillation;

• The best positioning accuracies are those given by the dual frequency PPP solutions, as the first order effect of the ionosphere is cancelled by using the ionospheric-free linear combination (Fortes 2002). For November 17, 2012, accuracies at the level of centimeters to a few decimeters for both RIOFE and RIOD are obtained. However, even with the availability of dual frequency observations, cycle slips caused by scintillation on the hardware receiver observations forced the

ambiguity resolution process to re-start several times along the four-hour session, generating a final coordinate difference RMS at the decimeter level. In case of the software receiver, the availability of fewer dual frequency observations in each epoch during 1h38min45s caused the solution not to converge to centimeter level in all coordinates during the duration of the sub-session.

Table 3 RMS and mean values of the differences of the NRCan CSRS-PPP Service kinematic mode coordinates to the reference coordinates at RIOFE (IF Data) and RIOD (Trimble) for all observation sessions

Station Type of PPP Solution

Difference to known coordinates (m) A posteriori code

standard deviation

Latitude Longitude Height

RMS Mean RMS Mean RMS Mean

Session: June 04-05, 2012 (no scintillation) RIOFE L1 code 0.7 0.2 0.8 -0.4 1.9 -0.3 0.9 RIOD L1 code 0.4 0.2 0.3 -0.1 0.9 -0.2 0.5

Session: October 24, 2012 (strong scintillation) RIOFE L1 code 3.9 -2.1 3.8 3.1 7.9 -1.6 2.8 RIOD L1 code 4.2 -2.3 4.0 3.2 6.9 -1.3 2.9

Session: November 17, 2012 (strong scintillation) RIOFE L1 code 3.2 -1.5 2.8 1.5 4.3 1.4 2.6 RIOD L1 code 3.3 -1.7 3.1 2.0 4.2 1.5 2.4

RIOFE L1 L2 code

(C/A and L2C) and phase

0.16 0.15 0.07 -0.05 0.33 0.25 - x -

RIOD L1 L2 code

(C/A and L2P) and phase

0.24 0.03 0.13 0.06 0.12 0.01 - x -

Session: February 20, 2013 (strong scintillation) RIOFE L1 code 2.5 -1.6 2.2 1.8 3.8 2.1 2.1 RIOD L1 code 2.6 -1.6 2.1 1.7 4.2 2.2 2.0

Session: March 28, 2013 (strong scintillation on fewer satellites) RIOFE L1 code 4.6 -3.6 2.0 1.3 8.1 5.8 2.2 RIOD L1 code 4.8 -3.8 1.8 1.4 8.2 5.8 2.0

Conclusions The results of this study indicate that hardware receiver technology advancements since the previous solar maximum led to the reduction of the percentage of L2 missing observations from 41% to 13%, i.e., by a factor of 3, considering the hardware/firmware models used in 2002 and 2012. In terms of current L2 tracking and cycle slips performance, scintillation contributed to an increase of the number of missing L2 observations by a factor of 2 and to the occurrence of cycle slips. Further tests using a state-of-art hardware receiver confirmed that, even with all the improvement, equatorial ionospheric scintillation is still an issue for this type of receiver during some periods.

Regarding the software receiver, the associated flexibility has allowed optimizing GPS signals’ acquisition and tracking under scintillation conditions while maintaining a standard receiver architecture. As a result, the performance of GSNRx™, which utilizes the shared-channel architecture, in the observation domain was excellent, with no actual cycle slip generated in any observation session and an overall percentage of missing L2 observations up to only 4%, even under severe scintillations.

This performance indicates that the current state-of-art of acquisition and tracking algorithms is highly resistant to equatorial ionospheric scintillation.

Submitting L1 code observations generated by the software receiver at RIOFE and by the hardware receiver at RIOD to CSRS-PPP service gave very similar results in the position domain in all cases, with the ionospheric delay being responsible for degrading coordinate accuracies from the session with no scintillation to those with strong scintillation by a factor up to 12. The CSRS-PPP L1 code results were also biased by up to four (horizontal) and six (vertical) meters in all sessions under strong scintillation at both stations.

The best positioning accuracies were those given by the dual frequency PPP solutions, at the centimeter to a few decimeters level for both RIOFE and RIOD in kinematic mode. However, cycle slips caused by scintillation on the hardware receiver observations forced the ambiguity resolution process to re-start several times along the session, degrading the coordinates. This fact did not happen when CSRS-PPP processed the software receiver cycle slip-free observations. Acknowledgements Thanks to Professor Mark Petovello, Dr. Fatemeh Ghafoori, Dr. James Curran, PLAN Group; Dr. Sonia Costa, Dr. Cristina Lobianco, Rodrigo Quirino, Alberto Luis da Silva, Mario Souto, IBGE; Pierre Tétreault, NRCan; and Professor João Francisco Galera Monico, CIGALA and Unesp, for their support. References Carrano CS, Groves KM, McNeil WJ, Doherty P (2012) Scintillation Characteristics across the GPS Frequency Band. Proc. ION-GNSS-2012, Institute of Navigation, Nashville, September, pp 1972-1989 Fortes LPS (2002) Optimising the Use of GPS Multi-Reference Stations for Kinematic Positioning. PhD Thesis, University of Calgary Fortes LPS, Luz RT, Pereira KD, Costa SMA, Blitzkow D (1998) The Brazilian Network for Continuous Monitoring of GPS (RBMC): Operation and Products. In: Brunner FK (ed) Advances in Positioning and Reference Frames, International Association of Geodesy Symposia, Springer, vol 118, pp 73-78 Klobuchar JA (1996) Ionospheric Effects on GPS. In: Parkinson BW and Spilker Jr. JJ (ed) Global Positioning System: Theory and Applications. American Institute of Aeronautics and Astronautics, vol I, pp 485-515 Oron S, D’ujanga FM, Ssenyonga TJ (2013) Ionospheric TEC variations during the ascending solar activity phase at an equatorial station, Uganda. Indian J Radio Space Phys 42(1):7-17 Petovello MG, O’Driscoll C, Lachapelle G, Borio D, Murtaza H (2008) Architecture and Benefits of an Advanced GNSS Software Receiver. J Glob Position Syst 7(2):156-168

Rebischung P, Griffiths J, Ray J, Schmid R, Collilieux X, Garayt B (2012) IGS08: the IGS realization of ITRF2008. GPS Solut 16(4):483-494. doi 10.1007/s10291-011-0248-2 Rezende LFC, de Paula ER, Kantor IJ (2007) Mapping and Survey of Plasma Bubbles over Brazilian Territory. J Navig 60(1):69-81. doi:10.1017/S0373463307004006 Sreeja VV, Aquino M, Forte B, Elmas Z, Hancock C, Franceschi G, Alfonsi L, Spogli L, Romano V, Bougard B, Monico JFG, Wernik AW, Sleewaegen JM, Canto A, Silva EF (2011) Tackling ionospheric scintillation threat to GNSS in Latin America. J Space Weather Space Clim 1(1):A05-1:9. doi: 10.1051/swsc/2011005 Van Dierendonck AJ, Klobuchar J, Hua Q (1993) Ionospheric Scintillation Monitoring Using Commercial Single Frequency C/A Code Receivers. Proc. ION-GPS-1993, Institute of Navigation, Salt Lake City, September, pp 1333-1342 Xu R, Liu Z, Li M, Morton Y, Chen W (2012) An Analysis of Low-Latitude Ionospheric Scintillation and Its Effects on Precise Point Positioning. J Glob Position Syst 11(1):22-32. doi: 10.5081/jgps.11.1.22

Biographies

Dr. Luiz Paulo Souto Fortes holds a BSc in Cartographic Engineering (IME 1981), an MSc in Computer Science (IME 1997), and a PhD in Geomatics Engineering (University of Calgary 2002). He works for the Brazilian Institute of Geography and Statistics (IBGE) since 1982 and also as an Associate Professor at the University of the State of Rio de Janeiro (UERJ) since 2005. He was a Visiting Researcher at the University of Calgary during 2012-2013

Dr. Tao Lin holds a BSc in Geomatics Engineering (University of Calgary 2008) and a PhD in Geomatics Engineering (University of Calgary 2013). He works as a post-doctoral fellow and senior research associate in PLAN group at University of Calgary since 2013. His research expertise includes GNSS software receiver and navigation algorithm design

Professor Gérard Lachapelle holds a Canada Research Chair in Wireless Location in the PLAN Group of the University of Calgary. He has been involved with GPS developments and applications since 1980. His research ranges from precise positioning to GNSS signal processing. More information is available on the PLAN Group website (http://plan.geomatics.ucalgary.ca)

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