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BENEFITS OF GNSS IF DATA RECORDING
Gérard Lachapelle and Ali Broumandan
Schulich School of Engineering
PLAN Group (http://plan.geomatics.ucalgary.ca)
University of Calgary
2500 University Drive, N.W., Calgary, Alberta, Canada, T2N 1N4
Abstract — The advantages of recording IF (intermediate
frequency) data when observing GNSS signals are discussed. This
fundamental data can be reprocessed indefinitely with any
acquisition and tracking loop algorithm to assist in the design and
use of enhanced loops for the applications needed. If a sufficient
number of bits and signal bandwidth are recorded, no
fundamental information is lost and the data can be used in postmission for advanced earth observations and GNSS infrastructure
protection used methods yet to be discovered. The data storage
requirement is described and two examples are employed to
demonstrate its use.
Keywords: — GNSS, data recording, signal analysis,
scintillation, tracking loop, vulnerabilities
I. INTRODUCTION
Digitized IF data is the first and most fundamental data
available following GNSS antenna signal receipt. Due to its
high density, digital data is used by receivers to derive lower
density measurements (e.g. code and carrier phase) with
algorithms specific to each receiver model. Since this digital
data is not recorded, it cannot be reprocessed later to obtain
higher quality derived measurements with more sophisticated
algorithms. Information contained in such fundamental data
is lost and cannot be recovered. Figure 1 shows the different
processing stages of a receiver along with the data rate in each
stage. The down-converted RF signals to IF (or baseband) in
the front-end are then digitized using an analog-to-digital
converter (ADC) at a suitable sampling rate. The quality of
the antenna, noise figures of the RF front-end and the
frequency and phase response of the filters in the RF frontend influence the quality of the received signals. The
sampling rate is proportional to the bandwidth of the signals
to be processed in order to achieve the performance
requirement of a given application. Generally, high accuracy
receivers require a wider bandwidth such that almost full
power of the signals is captured. The sampling rate in GNSS
applications may vary from a few mega samples per second
(MSPS) to a few tens of MSPS. The number of quantization
bits during analog-to-digital conversion is selected based on
allowable signal loss and processing power available for
further processing. Acquisition and tracking algorithms are
specific to receivers/ companies and are proprietary. They are
developed to be cost effective in term of computational power
requirements and to meet the application at hand. Hence they
are not the most sophisticated signal processing algorithms
available in term of reducing code and carrier phase noise and
multipath due to cost; the best algorithms may not be
available to the relevant manufacturer due to patent
restrictions and trade secrets. Most important is that
acquisition and tracking algorithm performance are
improving continuously as the past 30 years have positively
demonstrated and will likely continue to improve.
Due to IF measurement storage requirements, positions,
velocity and time (PVT) solutions are stored in most
receivers, such as in handheld units and smartphones.
Medium to high grade receivers store in addition
pseudorange, carrier phase and associated measurements. A
prime example is the International GNSS Service (IGS),
which has been storing pseudorange and carrier
measurements in its world-wide network for decades. These
are an invaluable source of information to derive and
maintain the primary earth reference frame and other
information. This would not be possible if only PVT solutions
would have been stored. As the data density that is stored is
decimated from IF to code and carrier to PVT solutions,
information is irretrievably lost. Hence what would be the
advantages of storing IF data and with what storage
requirement? The second question is addressed in the next
section. The first one is summarized below, with examples
provided later in the paper. IF data allows one to reprocess it
at will, at any computational speed in post-mission, using any
signal acquisition and tracking algorithms, including those
yet to be invented at future dates. This would permit the
recovery of much more information that can be obtained
using pseudorange and carrier phase data. During the first 10
years of GPS operation, this data was deemed useful mostly
for accurate geodetic positioning solutions to maintain
national and world reference frames; continental and regional
crustal motion and deformation could be effectively studied,
hence the beginning of GNSS derived earth observations; the
IGS network is an extraordinary achievement. Then GNSS
signals started to be used for atmospheric studies; early
storage of IF data positively demonstrated its advantage for
such studies. Some regional/local networks are now
collecting IF data for this purpose, often over limited periods
of time. A relatively new discipline exploiting GNSS signals
for earth observations is reflectometry, ranging from wind
speed [3] to sea roughness estimation [4]; these applications
are independent from positioning and timing and were not
anticipated by GPS developers. What other disciplines might
emerge in the years ahead using GNSS signals? Isn’t the
value of systematically storing IF data continuously on
worldwide networks truly exciting to provide long term data
series from which earth, climate, atmosphere might be
investigated over time?
In the sequel, two applications will be described to illustrate
the advantages of IF data capture. The first one deals with a
forensic analysis of GNSS signals affected by electronic
interference, the nature of which is assumed unknown a
priori. The second is a study of ionospheric scintillation in
Brazil in 2012-13 where IF data was recorded during selected
periods.
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Figure 1: Data Capture Points in a GNSS Receiver
The first application would normally require short term IF
data storage depending on the infrastructure requiring
protection while the second requires medium to long term,
the latter in anticipation of techniques yet to be discovered.
II. IF DATA STORAGE REQUIREMENTS
As shown in Figure 1 the data rate is higher at the first
receiver processing stage and reduces significantly along the
processing stages. As it moves through various processing
stages, quality is increasingly and irreversibly affected by the
algorithms used. Hence, stored digital data offers greater
flexibility in terms of using different algorithms, but with a
higher storage requirement compared to data stored at other
processing stages. The digital data storage requirement
depends on the number of constellations that are not sharing
the same frequency band, number of frequency bands in each
constellation to be captured, and number of bits used to
represent each digital sample. Many standard receivers use 2bits quantization because the quantization power loss is about
0.5 dB compared to 1.96 dB with 1-bit quantization [14].
However, if the applications need high quality of code and
carrier phase measurements, or is related to monitor higher
dynamic range of the input signal power, then a higher
number of quantization bits is needed. Typical examples
include anti-jamming receivers and weak signal processing to
mitigate cross-correlation. Current high-end receivers use 4
to 6 bits. A 16-bit ADC captures GNSS signals with a power
variation between ±49 dB from their nominal received power
on the surface of the earth, with negligible quantization power
loss [15]; this would theoretically be the ideal number of bits
for earth observations, although at a high storage cost.
Experts from various fields would have to weigh in to assess
the most cost effective option. Digital data can be stored in
any suitable format, e.g. the GNSS SDR Metadata Standard
[13].
The memory required to store digital data can be calculated
as
2 Bandwidth  ( bits / sample )
[Tera Bytes (TB)]
Memory 
8  1012
Here, the sampling frequency is chosen as twice the
bandwidth of the signal according to the Nyquist sampling
theorem. For instance considering 16 bits per sample and full
available bandwidth on each signal, the memory required to
store the digital data for individual GNSS signals namely
GPS, Galileo, GLONASS, BeiDou, QZSS and IRNSS at
different frequency bands over a 24-hour day per antenna is
shown in Figure 2 [5-12]. In the case of GLONASS FDMA
the entire signal bandwidth was considered.
Figure 2: Memory requirement per day for individual
signals (16-bit assumption)
The stability and phase noise of the reference oscillator
affect the quality of the sampled signals. The phase noise of
sampling clock, derived from the reference oscillator, used by
the ADC decreases the SNR of the sampled signals which in
turn leads to decreased C/N0 of the GNSS signals [1]. The
magnitude of the SNR loss increases with the increase in
sampling frequency for a given phase noise of the referece
oscillator. Also, it affects the code and carrier tracking loops
in the GNSS receiver, deteriorating achievable performance
[18]. The stability of the oscillator permits longer coherent
integration intervals leading to higher sensitivity. Hence, good
quality oscillators, which are still relatively costly, large and
power consuming, need to be chosen for optimal data quality.
III. EXAMPLE 1: INTERFERENCE FORENSIC ANALYSIS
GNSS signals can be easily disrupted by various types of
interfering signals including jamming and spoofing signals.
In GNSS-based critical applications, such as aircraft, train
and ship collision avoidance, it is required to investigate the
possible sources of error/malfunction in case of incidents or
accidents. A receiver may have produced faulty
measurements or an external intentional or unintentional
sources may have been the cause. The use of IF samples
available throughout the event allows a more in-depth
analysis. To demonstrate the effectiveness of this approach,
two laboratory tests were performed to investigate the
robustness of a commercial receiver to an intentional radio
frequency attack and then to analyze the benefits of IF
samples analysis to further understand the cause of the
disruption on the PVT solution. The first test investigates the
robustness of PVT solutions of the receiver under a wideband
(1)
jamming attack. The second test examines the effect of a
spoofing attack.
A. Jamming attack:
The experimental setup and data collection apparatuses for
the first test are shown in Figure 3. The authentic GNSS
signals via a roof top antenna were fed to a two-way
combiner. The second input of the combiner was connected
to a Rohde & Schwarz (R&S) SMBV100A hardware
simulator generating high power GNSS signals at L1 band as
a wideband jammer. The combined signals after power
adjustment were sampled by a National Instrument (NI)
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Figure 3: Data collection scenario
Figure 5: C/N0 values of authentic signals
front-end utilizing a 16-bit digitizer. In this data set jamming
power was gradually increased to finally mask the authentic
signals and then decreased to its initial power level. The
combined signals were fed to a two-way splitter. One of the
outputs was connected to a GNSS receiver and another output
was connected to the NI front-end. This type of interference
attack does not disturb the receiver while the jamming power
is equal or a bit higher than that of the authentic signals.
Nevertheless, if the jamming power significantly increases, it
severely affects the acquisition and tracking modules of the
receiver. Figure 4 shows the positioning errors of the receiver
as a function of time. The position and navigation solutions
were denied during a 150 s period. Figure 5 shows the
authentic signal C/N0 values reported by the receiver,
confirming tracking issues during the disruption period.
However, the latter is not know at this stage.
Time/frequency analyses of IF samples may be used to
characterize the error source. Figure 6 shows the standard
deviation (std) of IF samples as a function of time. It is around
1 as it should be when no interference occurs. The IF samples
power increases to 30 dB during the data collection. This
analysis confirms that the signal is affected by a high power
interference signal during the period starting at 100 s as
opposed to signal outage, antenna, cable or any other physical
hardware/software malfunction. A spectral analysis of the IF
samples at 1 and 270 s from the beginning of the data is
shown in Figure 7 for a 5 MHz signal bandwidth. The GPS
carrier centre frequency is shifted by 420 KHz (IF frequency).
The spectrum of the signal does not show a high power peak
at the frequency band hence the signal is not affected by a
narrowband jammer. The signals spectrum at time t=270 is
similar to that of BPSK modulation. This is not the case for
authentic GPS as the signals are below the noise floor. This
analysis demonstrates a possible wideband jamming attack
utilizing BPSK modulation.
Figure 6: IF samples standard deviation profile during the
data collection
Figure 4: Receiver position solution errors
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Figure 7: Power spectral density of IF samples
Figure 8: East, North and Up errors as a function of time
B. Spoofing attack:
This test was performed utilizing a hardware simulator (HS).
The HS can simulate two independent receiver antennas from
a single simulation scenario simultaneously. To simulate a
realistic spoofing scenario one of the HS ports simulated a
static receiver as the authentic receiver. The other port
outputted a rover receiver scenario moved on a 1000 m side
square trajectory where the authentic receiver was located in
one of its corner. The spoofing signals had a 1-3 dB power
advantage over the authentic signals. The output signals of
the ports were fed to a RF combiner and then passed to a RF
splitter. The same as the previous test the output signals from
the splitter were fed to the same receive and NI front-end. The
spoofing signals was initially disconnected for about 150 s.
During this period the receiver was locked on the authentic
signals. After about 150 s form the beginning of the data
collection, spoofing signals were injected to the simulation
scenario. At that epoch, the Euclidean distance of the spoofer
location from the authentic one was about 1400 m (the
spoofer and authentic were located on opposite corners of the
square trajectory). In this case the spoofing and authentic
signal correlation peaks did not overlap for most PRNs.
Figure 8 shows the East, North, Up errors of the position
solution of the receiver as a function of time. As shown, the
existence of the spoofing signal during the first 300 s period
did not disrupt the position solution. This is due to the fact
that the receiver was locked on authentic signals and the
existence of spoofing signals did not affect the authentic
signal tracking performance. After 300 s the spoofing signals
succeeded in grabbing the tracking point of the authentic
signals and in deceiving the position solutions. If an
unexpected event occurs, investigating the navigation
solution does not provide any information regarding its cause.
Having access to the IF samples, further analyses can be
performed to characterize the nature of the accident. Figure 9
shows the power spectral density plots at different time of the
data collection scenario.
The spectral density is increased at t=400 compared to that at
t=1. The power spectral density plot of the signal at t=400 is
similar to that of BPSK modulation with a 2 MHz main peak
bandwidth. This is not the case for authentic signals as
compared to the spectral density plot at t=1. Hence, there is a
high possibility of existence of more than normal BPSK
modulated signals (e.g. spoofing attack) in the desired
frequency band. Post correlation analyses can be used to
further characterize the interference source. Figure 10 shows
a view of correlator outputs in the case of PRN2. There are
two correlation peaks as opposed to one distinctive peak for
the authentic case. Appearance of more than one correlation
peaks at the output of the cross ambiguity function is one of
the spoofing detection metrics. Considering the above
analysis it can be concluded that the cause of the erroneous
solutions was a spoofing attack.
IV. EXAMPLE 2: IONOSPHERIC SCINTILLATION ANALYSIS
As a result of high level solar activity, ionospheric
scintillation occurs and causes amplitude and phase
fluctuation of GNSS signals received on the earth’s surface.
The effect is inversely proportional to frequency and GNSS
frequencies were chosen sufficiently high to avoid much of
the effects seen at lower frequency (as it was the case for the
NNSS Transit Doppler system). Nevertheless, effects
affecting carrier phase measurements remain during the
above periods, which reach a maximum every 11 years.
Hence, every 11-year period, occasional carrier phase
measurement issues occur on satellite signals traversing parts
of the ionosphere that are disturbed. However, with the
number of satellites available increasing, the likelihood of
significant carrier phase positioning performance issues
decreases.
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Figure 11: Example of S4 values for satellites above 10˚
elevation
Figure 9: Power spectral density of IF samples
Figure 10: Authentic and spoofing signals correlation peaks
Added to this is the increasingly robust and sophistically
carrier phase tracking loops used by receivers. Code lock
loops producing pseudorange measurements are not affected
directly, although a higher level of ionospheric activity
results in higher code delays. Since the delays are a function
of frequency, the use of two frequencies is used to remove it.
IF data is now used commonly by researchers investigating
the effect of the ionosphere on GNSS signals. The advantage
is that enhanced tracking loop algorithms can be tested with
the same data as often as needed to test performance. Leading
manufacturers have been doing just this and the remarkable
improvement of their carrier phase measurements under
ionospheric scintillation during the past 25 years are well
known. An example of such research is the joint project
undertaken by the University of Calgary and Brazil’s IBGE
(Instituto Brasileiro de Geografia e Estatística) in 2012-13
reported by [2]. A solar maximum was predicted to occur
(and did occur) in 2013.
An IF data collection system was set up in Rio de Janeiro
during anticipated periods of scintillation from June 2012 to
March 2013. The choice of the location was ideal as
equatorial scintillation is generally the most significant. A
GPS L1/L2C IF data recording system was used for this
purpose. Due to various hardware limitations, only 1-bit
recording was performed, which was to prove sufficient to
accomplish the investigations intended.
The level of ionospheric scintillation is best characterized by
a parameter called S4 [19]. The theoretical maximum is 1,
although values slightly above 1 sometime occurs due to
various error contributions. An example is shown in Figure
11 for October 24, 2012, a scintillation day considered high
in Rio. S4 values above 0.6 indicate strong to very strong
scintillation. Satellites with S4 values below 0.4 are only
affected mildly or not at all due to their positions relative to
the band of the ionosphere affected by scintillation that day.
The 1-bit IF data for the above day and others of high and low
scintillation was processed using various aided phase lock
loops implemented in the GSNRx software receiver [17] for
this purpose as described in [16]. The IF data was used to test
and enhance these loops; the ability to reprocess the IF data
during the loop aiding and tuning process proved invaluable.
The shared-architecture tracking loop described in [16] was
selected as the most effective. The results reported in [2]
show no carrier phase date loss on L1 and only up to 4% on
L2C for satellites affected by ionospheric scintillation when
high.
V. CONCLUSIONS
The two examples described herein illustrate well the
advantages of recording GNSS IF data for applications
ranging for scientific studies to protection of the
infrastructure for safety of life applications. The two
applications discussed required a limited number of bits and
limited storage periods. Others, some yet to be discovered,
would require more bits and long storage periods. It would
appear that starting with 4 bits long term storage as an
example would address many of current and anticipated
scientific uses. Storage costs and data access methods would
become issues if large global GNSS networks were to be
considered.
The long-term benefits would likely be
proportional to the level of commitment involved.
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