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Novel Multipath Mitigation Methods using a
Dual-polarization Antenna
Paul D Groves, Ziyi Jiang, Benjamin Skelton, Paul A Cross
University College London, United Kingdom
Lawrence Lau
Institut De Geomatica, Barcelona, Spain
Yacine Adane, Izzet Kale
University of Westminster, London, United Kingdom
BIOGRAPHY
Paul Groves is a Lecturer (academic faculty member) in
GNSS, Navigation and Location Technology at University
College London (UCL). He was a navigation systems
researcher at QinetiQ from 1997 to 2009. He is interested
in all aspects of navigation and positioning, including
multi-sensor integrated navigation and robust GNSS under
challenging reception conditions. He is an author of more
than 30 technical publications, including the book,
Principles of GNSS, Inertial and Multi-Sensor Integrated
Navigation Systems (Artech House). He holds a BA/MA
and a DPhil in physics from the University of Oxford. He
is a Fellow of the Royal Institute of Navigation and chairs
its R&D group. He is also an associate editor of
Navigation: Journal of the ION. (p.groves@ucl.ac.uk)
Ziyi Jiang is a Research Fellow at UCL, currently
specialising in multipath mitigation research. He has
recently submitted his PhD thesis on digital route model
aided integrated satellite navigation and low-cost inertial
sensors for high-performance positioning on the railways.
He holds a BEng in measuring and control technology
from Harbin Engineering University, China.
Benjamin Skelton is completing a MSc in surveying at
UCL, including a research project studying dual-
polarization GNSS antennas. He holds a LLB in Law from
the University of Leicester, UK. He appears in Figure 3.
Paul Cross is a visiting professor at UCL, having served as
Professor of Geomatic Engineering from 1997 until his
retirement in 2009. He obtained his PhD from the
University of Nottingham in 1970 and has held teaching
and research positions at the Universities of Nairobi, East
London, Stuttgart and Newcastle. His main research
interest is in precise GNSS positioning.
Lawrence Lau is an Associate Professor in the Institute of
Geomatics (IG) in Barcelona. Before joining IG, he was a
Research Fellow in the Department of Civil,
Environmental and Geomatic Engineering at UCL
(September 2004 - February 2010). He received a PhD
from UCL in 2005. His current research is concerned
with investigations into multipath modelling and its
mitigation techniques, and high precision multiple-
frequency GNSS data processing algorithms, especially
for RTK and point positioning applications.
Yacine Adane is a Research Fellow at the University of
Westminster, specialising in radio frequency engineering.
He received the Eng. degree in Electrical Engineering
from the Science and Technology University, Algiers in
1999. He obtained his Master’s Degree in 2001 and his
PhD in 2004 from the Pierre and Marie Curie University
(Paris VI), Paris. During his PhD studies, based at France
Télécom, he developed fast and efficient algorithms for
near field evaluation of base station antennas. Since 2005,
he has worked on various projects developing GNSS
receivers and simulators. He has authored more than 20
papers on RF and antennas. He is the recipient of the best
student paper award at the 2004 IEEE Mediterranean
Microwave Symposium and co-recipient of the 2009
Innovation Lord Stafford Award.
Izzet Kale is Professor of Applied Digital Signal
Processing and Very-Large-Scale Integration at the
University of Westminster and the director of Applied
DSP and VLSI Research Group (ADVRG). He joined the
staff of the University in 1984 and he has been with them
since. He holds a BSc in electrical and electronic
engineering from the Polytechnic of Central London an
MSc in microelectronic systems from Edinburgh
University and a PhD in techniques for reducing digital
filter complexity from the University of Westminster. His
research interests include the implementation of efficient
low-power DSP algorithms for use in the positioning,
communications and biomedical industries.
ABSTRACT
There are many methods for mitigating GNSS multipath
errors. However, none of them completely eliminate the
effects of multipath or suit all GNSS applications. A new
class of multipath mitigation methods exploit new dual-
polarization antenna technology. GNSS signals received
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direct from the satellites have right-handed circular
polarization (RHCP), whereas (singly) reflected signals
have left-handed circular polarization (LHCP) or an
elliptical polarization that may be expressed as the sum of
RHCP and LHCP components. Conventional GNSS user
antennas are more sensitive to signals with RHCP,
attenuating LHCP signals and reducing, but not
eliminating, the multipath errors in the receiver. An
antenna with the opposite polarization sensitivity will
attenuate the direct signals more than the reflected signals.
This can be used to characterizing the reflected signals
and thus mitigate the effects of multipath interference.
Experimental work using an Antcom dual-polarization
antenna and dual geodetic receivers is presented. This
verifies that carrier power to noise density, C/N0,
measurements obtained by separately correlating the
RHCP and LHCP antenna outputs can be used to
distinguish between a low-multipath and moderate-
multipath environment. This may be used as the basis of a
multipath detection technique.
Three different multipath mitigation techniques that use a
dual-polarization antenna are proposed. Measurement
weighting estimates the code and carrier multipath error
standard deviation from the RHCP-LHCP C/N0 difference
and elevation angle. This is used by the navigation
processor to discard and reweight measurements. Range-
domain multipath correction, uses the pseudo-range,
carrier-phase and C/N0 differences between the outputs of
RHCP and LHCP receiver tracking channels, together
with antenna calibration data, to estimate corrections to
the code and carrier measurements. In tracking-domain
multipath mitigation, the RHCP and LHCP correlator
outputs are input to common acquisition and tracking
algorithms which attempt to separate the direct line of
sight and reflected signals
The design of a novel dual-input GNSS front end, based
on direct RF sampling, is presented This will be used, in
conjunction with a software GNSS receiver, for future
development and testing of multipath mitigation using a
dual-polarization antenna.
1. INTRODUCTION
Multipath interference is a significant limiting factor on
the accuracy of GNSS for a host of applications, ranging
from personal positioning in urban areas to precise land
surveys.
Reflected signals distort the code correlation peak within
the receiver such that the code phase of the direct line-of-
sight (LOS) signal can not be accurately determined by
equalising the power in the early and late correlation
channels. The resulting code tracking error depends on the
receiver design as well as the direct and reflected signal
strengths, path delay and phase difference, and can be up
to half a chip [1] [2] [3]. Multipath interference also affects
carrier phase determination, producing errors of up to
quarter of a wavelength. Multipath errors can be positive
or negative, depending on the phase difference between
the direct and reflected signals.
A related phenomenon, sometimes wrongly classified as
multipath, is non-line-of-sight (NLOS) signal reception.
This occurs when the direct LOS signal is blocked and
only reflected signals can be received. Pseudo-range
measurement errors from NLOS signals are always
positive and theoretically unlimited.
A number of methods exist for mitigating the effects of
multipath and NLOS propagation on code and carrier
measurements. However, no method is applicable to all
applications or eliminates multipath errors completely.
This paper describes an emerging class of multipath
mitigation methods that exploit new dual-polarization
antenna technology. These may be used either alongside
or in place of conventional multipath mitigation
techniques. GNSS signals received direct from the
satellites have right-handed circular polarization (RHCP),
whereas (singly) reflected signals have left-handed
circular polarization (LHCP) or an elliptical polarization
that may be expressed as the sum of RHCP and LHCP
components [4]. Conventional GNSS user antennas are
more sensitive to signals with RHCP, attenuating LHCP
signals and reducing, but not eliminating, the multipath
errors in the receiver. An antenna with the opposite
polarization sensitivity will attenuate the direct signals
more than the reflected signals. For producing an accurate
position solution, this is clearly undesirable. However, it is
useful for detecting and characterizing the reflected
signals.
Dual-polarization GNSS antennas are now commercially
available, combining coaxial RHCP-sensitive and LHCP-
sensitive antennas in a single housing with dual outputs
[5]. In the new multipath mitigation approach, the signals
received from the two antennas are correlated separately
within the receiver. The measurements are then compared
in order to detect and calibrate the multipath errors.
This paper begins with a review of existing multipath
mitigation techniques and a summary of prior work with
dual-polarization antennas. Section 2 then presents and
discusses the results of initial multipath detection
experiments with an Antcom dual-polarization antenna [5]
and dual Leica 500-series geodetic GPS receivers. Section
3 proposes a number of multipath mitigation techniques
using a dual-polarization antenna system. Section 4 then
describes the development of a new dual-input GNSS RF
front-end suitable for implementing the new multipath
mitigation methods. Finally, Section 5 presents
conclusions and discusses future work.
Established multipath mitigation techniques can be
classified into site-dependent, antenna-based, receiver-
based and measurement-processing techniques. Starting
with site-dependent methods, in-situ multipath
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calibrations for reference stations may be made based on
the repeatable satellite-reflector-antenna geometry in
about one sidereal day [6] [7]. In multipath environmental
modelling, a ray-tracing algorithm uses the known
satellite-reflector-antenna geometry and physical
properties of reflectors to determine the phase multipath
errors [8]. Site-dependent techniques are highly effective
at mitigating multipath, but are generally only suited to
static receivers. A similar approach has been applied to
mitigating multipath reflections off satellite bodies for
attitude determination [9] [10].
Antenna-based multipath mitigation techniques include
special antenna designs such as choke-ring antennas,
Trimble’s Zephyr antennas and the multipath-limiting
antenna for ground-based augmentation system reference
stations [11]; these reduce the gain of signals reflected off
the ground. Antenna array techniques, based on the
geometric correlation of multipath errors at closely-spaced
antennas, can be used for more general multipath
mitigation [12] [13]. However, they perform best under
simple multipath environments and are not suited to most
kinematic applications because antenna arrays are usually
bulky.
A number of receiver-based techniques have been
developed that mitigate code multipath errors by
increasing the resolution of the code discriminator on the
basis that the higher-frequency components of a GNSS
signal are less impacted by moderate-delay multipath
interference. The simplest approach is to use a narrow
early-late correlator spacing [14], while a more
sophisticated method is the Multipath Mitigation Window
(MMW) [15]. Most of these techniques can effectively
mitigate multipath where the path delay of the reflected
component is more than 7.5 m, while the Vision
Correlator will operate at path delays down to 5 m [16].
However, these two techniques operate at the expense of
signal-to-noise ratio performance [17]. Moreover, they
are not designed to mitigate the effects of multipath on
carrier-phase measurements.
The final class of multipath mitigation technique operate
by processing the code and carrier measurements output
by the receiver. One approach is to use stochastic models
to weight measurements within the position solution
according to their multipath vulnerability
[18] [19] [20] [21] [22]. These models are based on the
correlation between the carrier power-to-noise density,
C/N0, and the multipath errors. For example, variations of
the phase multipath error and resultant C/N0 over time are
orthogonal [22]. The use of adaptive filters with spectrum
analysis has been investigated for estimating phase
multipath from C/N0 measurements. However, these
techniques require sinusoidal multipath patterns to build
up over time so are therefore only applicable for static and
very low dynamic applications.
A simple and effective method of reducing the effects of
code multipath errors is smoothing the pseudo-range
measurements with carrier-phase. This is most effective
where the time constant of the smoothing algorithm
significantly exceeds the correlation time of the pseudo-
range multipath errors. The final processor-based
multipath mitigation technique is application of integrity
monitoring techniques to identify multipath contaminated
code and carrier measurements through their
inconsistency with the uncontaminated measurements; this
should work better with multiple constellation receivers.
Multipath mitigation using dual-polarization antennas
spans three categories: antenna-based, receiver-based and
measurement-processing techniques. The use of dual
RHCP and LHCP antennas for studying multipath was
first proposed in [25] and results presented using a pair of
helical antennas. Multipath mitigation using a dual-
polarization antenna was demonstrated by simulation in
[26]. In [4], multipath mitigation using arrays of dual-
polarization antenna arrays was assessed by simulation. In
[27], it was validated, using another multipath detection
method, that the LHCP component of an Antcom dual-
polarization antenna receives greater reflected signal
power than the RHCP component.
2. MULTIPATH DETECTION EXPERIMENTS
A series of tests were conducted to assess the ability of a
dual-polarization antenna system to detect multipath
interference. The focus was on comparing the carrier
power to noise density, C/N0, measured from the LHCP
and RHCP antenna outputs and assessing how this varied
with elevation angle and multipath environment. Carrier
power to noise density is a measure of the signal to noise
ratio within the receiver’s correlators [1].
Following initial tests to determine the correct equipment
configuration, experiments were conducted to characterise
the behaviour of the system in a low-multipath
environment (LME). Tests were then performed in a
moderate multipath environment (MME) and the C/N0,
measurements compared with those obtained in the LME.
The hardware comprised an Antcom 3G1215RL-P-XS-1
dual-polarization L1/L2 GPS antenna, attached to a
standard tribrach mount with dual amplifiers, as shown in
Figure 1, together with a pair of Leica System SR530
geodetic GPS receivers, shown in Figure 2. One receiver
was connected to each polarization output of the antenna
via an amplifier powered from the receiver. Data was
logged to memory cards independently with GPS itself
used for time synchronisation between receivers.
The Leica receivers would only log measurement data to
the memory cards where sufficient signals were being
received to generate a position solution. In the default
high accuracy mode, it was typically only possible to track
1 or 2 satellites using the LHCP antenna output. However,
switching the receivers to the higher sensitivity
“MaxTrak” mode enabled 4 or more satellites to be
tracked for most of the time in open and sparse urban
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environments. It was also found that performance could be
improved by temporarily connecting the “LHCP” receiver
to the RHCP antenna output to aid downloading of the
ephemeris data.
Figure 1: Antcom 3G1215RL-P-XS-1 dual-polarization
antenna, mounting and amplifiers.
Figure 2: Dual Leica System SR530 geodetic GPS
receivers
A location within London’s Regent’s Park was selected as
a minimal multipath environment for testing the dual-
polarization user equipment. Although this location was
far away from buildings, there were trees nearby that
could affect reception of low-elevation signals. The first
tests were performed with the antenna mounted on a
tripod, as shown in Figure 3.
A comparison of C/N0 measurements made using the
LHCP and RHCP antennas showed greater variation than
expected at all elevation angles, mostly in the LHCP data.
Figure 4 shows an example at an elevation of 30°. Note
that the C/N0 measurements are quantised at 1 dB-Hz
intervals, which is a common feature of GNSS user
equipment designs. Signal to noise levels were too high to
attribute this to C/N0 measurement noise [28]. It was
therefore conjectured that multipath interference due to
reflections from the ground that might be the main cause
of the problem.
Figure 3: Data collection in Regent’s Park with a tripod.
To test the ground-multipath hypothesis further data was
collected with the tribrach mount placed directly on the
ground as shown in Figure 5. This successfully reduced
the standard deviation of the C/N0 measurements. Figure 6
shows an example at an elevation of 34°. However,
significant C/N0 measurement variation was still observed
at all elevation angles. Possible causes are residual ground
reflections and reflections off the antenna mount.
Therefore, mounting of the antenna on a ground plane or
using choke rings should be investigated.
Figure 4: Measured C/N0 using LHCP antenna output with
a tripod-mounted antenna (PRN 5, ~30° elevation)
Data for determining the distribution of the C/N0
difference in a low multipath environment. For the
purposes of this study, C/N0 difference is defined as the
RHCP antenna output C/N0 minus the LHCP output C/N0
in decibels. The sign convention is selected to give
positive numbers for positive elevation angles.
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Approximately 7 hours of data was collected over two
visits at different times of day.
Figure 5: Data collection in Regent’s Park with a ground-
based antenna.
Data logged from both receivers was imported into
Microsoft Access, within which the query function was
used to match the data from the same epoch and satellite.
The paired data was then transferred to Microsoft Excel,
where the C/N0 difference was calculated. Finally, the
MATLAB Curve Fitting Toolbox was used to estimate the
RHCP polarization discrimination as a function of
elevation angle from the measured C/N0 difference.
Figure 7 shows the measured C/N0 difference from the
Regent’s Park data with a ground-based antenna, together
with its mean and 95% bounds; these are at ±4.08 dB.
Figure 6: Measured C/N0 using LHCP antenna output with
a ground-based antenna (PRN 12, ~34° elevation)
For data collected in an unknown multipath environment,
the measured C/N0 difference may be compared with the
LME mean C/N0 difference at the corresponding elevation
angle to obtain an estimate of the level of multipath
interference. Four cases are considered:
1) If the measured C/N0 difference lies within the 95%
bounds of the LME C/N0 difference distribution, then
it may be assumed that the signal is likely to be subject
to a multipath level similar to that in the Regent’s Park
environment.
2) If the C/N0 difference is positive, but lies below the
LME C/N0 difference 95% bounds, then there is a
510 20 30 40 50 60 70 80
2
5
10
15
20
23
Elevation, deg
RHCP-LHCP C/N
0
difference, dB
C/N
0
difference measurements
Mean C/N
0
difference
95% C/N
0
difference bounds
Figure 7: Estimated RHCP polarization discrimination and measured C/N0 difference from Regent’s Park data with a ground-
based antenna
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23rd International Technical Meeting of the Satellite Division of
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significant probability that the signal is subject to a
more severe multipath environment than Regent’s
Park.
3) For C/N0 difference measurements that lie above the
LME C/N0 difference 95% bounds, more research is
needed to interpret their origin and to what extent they
are correlated with multipath errors on pseudo-range
and carrier-phase measurements derived from a RHCP
antenna output.
4) If the C/N0 difference is negative, then it is likely that
the direct LOS signal is blocked and only reflected
signals are being received from that satellite. Under
NLOS conditions pseudo-range and carrier-phase
measurement errors may be very large.
Further measurements were taken in the main quadrangle
at UCL. Figure 8 shows the location. Here, it is expected
that some GNSS signals will be affected by multipath
interference. Figure 9 shows the measured C/N0 difference
for signals from four satellites tracked over approximately
4 minutes, except for satellite PRN 29, which was only
tracked for about 40 seconds. Also shown in the figure is
the LME C/N0 difference mean and 95% limits..
The measured C/N0 difference for PRN 29 is clearly
consistent with the LME C/N0 difference, suggesting that
this signal is not seriously contaminated by multipath. For
PRN 31, more than half of the C/N0 difference
measurements lie below the 95% bounds of LME C/N0
difference, suggesting there is a significant risk that this
signal is multipath-contaminated.
Figure 8: Data collection in the main quadrangle at UCL
(One location depicted from two angles).
However, for PRN 21 and PRN 30, it is more difficult to
determine the multipath status. Although most of the C/N0
difference measurements lie within the 95% bounds of the
LME C/N0 difference, many lie above. Furthermore, the
large variation in the C/N0 difference over a few minutes
could be indicative of a problem. Further investigation is
needed.
A few negative C/N0 difference measurements were
observed in the UCL quadrangle when tests were being
conducted using aluminium foil to reflect the signals. It is
conjectured that transient negative C/N0 differences may
have been the experimenter obstructing the direct signal.
30 40 50 60 70 80
2
5
10
15
20
23
Elevation, deg
RHCP-LHCP C/N0 difference, dB
LME mean C/N
0
difference
LME 95% C/N
0
difference bounds
PRN 21 measured C/N
0
difference
PRN 29 measured C/N
0
difference
PRN 30 measured C/N
0
difference
PRN 31 measured C/N
0
difference
Figure 9: Measured C/N0 difference from UCL main quadrangle data compared with low-multipath environment data
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These experiments have verified that the difference
between RHCP and LHCP C/N0 can be used to identify
the likely presence of multipath interference on a GNSS
signal. However, the sensitivity is relatively poor due to
fluctuation in the LHCP C/N0 measurements even in low-
multipath environments. More research is needed to
determine whether antenna mounting modifications can
improve the sensitivity and whether the LHCP C/N0
fluctuation itself can be used to aid multipath detection,
particularly for static applications.
A further issue is that the polarization discrimination of
the antenna drops significantly for elevation angles below
about 45°. Where the plane of the antenna is horizontal,
low satellite elevation angles correspond to high angles of
incidence at the antenna. Reduced polarization sensitivity
at higher incidence is a feature of all circularly polarized
antennas. Consequently, poor performance at low
elevation angles is an inherent limitation of the dual-
polarization multipath mitigation technique. However,
with a multi-constellation GNSS receiver, there should
always be sufficient higher elevation signals to form a
navigation solution at most latitudes. Furthermore, higher
elevation signals are typically less likely to be
contaminated by multipath, less likely to be blocked by
buildings and are subject to smaller ionosphere and
troposphere propagation errors. Therefore, limiting the
GNSS user equipment to higher elevation signals is not
necessarily a major drawback.
3. MULTIPATH MITIGATION TECHNIQUES
Three different methods of mitigating multipath
interference are proposed. In order of increasing
sophistication, they are measurement weighting, range-
domain multipath correction and tracking domain
multipath correction. Each is discussed in turn and will be
explored further in future work.
Where NLOS signals are detected, as indicated by
negative C/N0 difference measurements, these should
typically be discarded from the navigation solution.
3.1 Measurement weighting
The simplest way of mitigating multipath interference is
simply to estimate the standard deviation of the code and
carrier multipath from the measured C/N0 difference and
the elevation angle and pass this to the navigation
processor. Figure 10 shows a suitable user equipment
architecture for this.
The navigation processor can then discard those
measurements subject to severe multipath interference and
appropriately reweight those subject to moderate
interference. The exact approach will vary with the
applications and the number of signals available without
multipath contamination.
The form of the model for determining multipath error
standard deviations from the C/N0 difference and elevation
angle may be derived theoretically. However, the
coefficients will need to be determined empirically to
account for variations in multipath errors with receiver
design, receiver-dependent C/N0 measurement artefacts
and antenna-mounting effects.
3.2 Range-domain multipath correction
Range-domain multipath correction is applied between the
outputs from the receiver’s ranging processor and the
inputs to the navigation processor. Measurements
available after the receiver ranging processor include
satellite pseudo-range measurements, carrier phase
measurements and C/N0 information. A multipath
correction mechanism based on comparing range-domain
measurements is proposed in this section.
A reflected or diffracted signal may be described by a
relative amplitude (or damping factor),
α
, range lag, Δ,
and carrier offset,
θ
m, with respect to the direct LOS
signal. The range lag and carrier offset are common to
signals received through both the RHCP and LHCP
antenna outputs. However, the relative amplitude differs
between polarization. The lag can also be expressed in
Figure 10: Dual-polarization GNSS user equipment architecture suitable to for measurement weighting and range-domain
multipath correction
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code chips using cfco /Δ=
γ
, where fco is the spreading-
code chipping rate and c is the speed of light.
The code tracking error in the presence of multipath can
be obtained by solving the equation: D = 0, where D is the
discrimination function. When using an early-minus-late
power (ELP) noncoherent discriminator, the equation, D
= 0, is written as
0
2222 =−−+ LLEE QIQI , (1)
where I and Q are, respectively, the in-phase and
quadraphase correlator outputs and subscripts E and L
denote the early and late correlation channels,
respectively. Assuming that there is only one delayed
signal, and the lag is small, the frequency offset is
negligible, and precorrelation band-limiting may be
neglected, an analytical solution to (1) can be written as
[1]
2
1cos2 cos
2
2dxx
m
m<−
++
+
=
δδ
θαα
θαα
(2)
Where x is the code tracking error in chips and d is code-
phase offset in chips between the early and late reference
signals.
The phase multipath error,
ψ
, caused by a single delayed
signal is given by [2]
⎥
⎦
⎤
⎢
⎣
⎡
+
=−
m
m
xRxR
θα
θα
ψ
cos)(1 sin)(
tan 1 (3)
where R is the pseudo-random noise (PRN) code
correlation function.
From (2) and (3), three unknown parameters are needed to
reconstruct the code and phase multipath error in the
RHCP signal: the RHCP damping factor,
α
RHCP , range
lag, Δ, and carrier offset,
θ
m. A fourth unknown is the
LHCP damping factor,
α
LHCP.
When using a dual polarization antenna, signals from both
RHCP and LHCP outputs can be processed by the receiver
using the architecture proposed in Figure 10. As can be
seen from the figure, signals received by RHCP and
LHCP outputs are processed in separate front-ends and
ranging processors, i.e. independent correlators and
tracking loops are used for each signal. NCO commands
are feedback to correlators to maintain the locked signals.
Measurements available in the range-domain are as
follows:
• Pseudo-range measurements from both polarizations,
ρ
RHCP and
ρ
LHCP;
• Carrier phase measurements from both polarizations,
φ
RHCP and
φ
LHCP;
• C/N0 measurements from both polarizations.
The range-domain measurements are closely linked to the
code and phase multipath errors. By differencing the
measurements between polarizations and then applying a
nonlinear model, the multipath parameters required to
determine the code and carrier multipath errors using (2)
and (3) may be determined.
Also required is an estimate of the RHCP polarization
discrimination. This is defined as the RHCP minus LHCP
C/N0 difference for a purely RHCP-polarized incident
signal. This is needed to determine the relative strength of
the direct LOS signal between the two antennas. Based on
communications with the manufacturer, the LHCP
polarization discrimination, defined as the C/N0 difference
for a purely LHCP-polarized incident signal, is assumed to
be equal and opposite to RHCP polarization
discrimination. However, this has not been independently
verified.
The best estimate of the RHCP polarization discrimination
obtainable from the tests described in Section 2 is the
LME mean C/N0 difference, shown in Figure 7. However,
as the 95% bounds of the LME C/N0 difference are ~4 dB,
there is almost certainly some signal reflection present.
Consequently, there could be a few dB difference between
the LME mean C/N0 difference and the RHCP
polarization discrimination. Therefore, the current best
estimate of the RHCP polarization discrimination is
insufficiently precise for determining accurate multipath
corrections. Improvements to the experimental
methodology are thus required in order to obtain a more
accurate RHCP polarization discrimination measurement.
Pseudo-ranges are formed based on signal transmission
time estimated in the DLL, which is dynamically
maintained using the measured code tracking error.
Carrier phase measurements are continuously updated
through PLL. The signal amplitudes are proportional to
the C/N0 information.
Deriving the multipath error parameters from the available
range-domain measurements is complicated by the
smoothing introduced by the receiver’s code and carrier
tracking loops. Furthermore, the Leica System SR530
GPS receivers only output carrier-smoothed pseudo-range
measurements. Consequently, the code multipath errors
are averaged over the order of a minute, while the carrier
multipath errors are averaged over less than a second. This
is likely to prevent determination of the multipath
corrections. It may be possible to use the carrier
measurements to recover the raw pseudo-ranges.
Otherwise, a different model of receiver must be used.
Note also that there is a risk of the LHCP receiver
channels tracking long-delay reflections and rejecting the
direct LOS signal altogether. This may be mitigated
through feedback from the RHCP channels.
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3.3 Tracking-domain multipath mitigation
The final approach to mitigating multipath using a dual-
polarization antenna is in the tracking domain. For each
signal tracked, the RHCP and LHCP antenna outputs are
separately correlated within the GNSS. However, both
sets of correlator outputs, the Is and Qs, are output to a
common set of acquisition and tracking algorithms. These
algorithms generate the numerically-controlled oscillator
(NCO) commands that control the reference code and
carrier signals for both the RHCP and LHCP correlation
channels. Figure 11 illustrates this. Tracking-domain
multipath mitigation may be thought of as “deeply-
coupled” and range-domain mitigation as “loosely-
coupled”.
Acquisition may use either the RHCP antenna signals
alone or a summation of the signals from the RHCP and
LHCP antennas. For tracking, the LHCP and RHCP Is and
Qs are input separately to an algorithm that tracks both the
direct LOS signal and the resultant reflected signal. Only
the direct LOS signal tracking is used to generate the
pseudo-range, pseudo-range rate and/or carrier phase
measurements used by the positioning algorithm. Note
that tracking-domain multipath mitigation also requires an
accurate estimate of the RHCP polarization
discrimination.
Two approaches may be considered. The first is to use
discriminator functions and separate tracking loops for the
direct code, direct carrier, reflected code and reflected
carrier, together with C/N0 measurement models for both
polarizations.
The second approach uses an extended Kalman filter
(EKF) or nonlinear estimation algorithm to estimate the
code phase, carrier phase, carrier frequency and signal
amplitude or C/N0 for both the direct and resultant
reflected signals. The Is and Qs from both sets of
correlators are input directly as measurements. A similar
multipath tracking filter for use with a single-polarization
multiple-correlator receiver is described in [27].
5. HARDWARE DEVELOPMENT
Using two separate receivers to process data from a dual-
polarization antenna is cumbersome. Range-domain
multipath mitigation must account for the timing
difference between the two receiver clocks, while
tracking-domain multipath mitigation can not be
performed. Further limitations of the Leica receivers are
that raw pseudo-range measurements are not available;
only carrier-smoothed pseudo range, and that data can
only be logged where sufficient signals are being received
to generate a position solution.
Therefore, a bespoke dual front-end GNSS receiver is
being developed for use in further studies of dual-
polarization multipath mitigation.
In the current context of having the GNSS bandwidth
occupied by several signals coming from the satellites in
orbit, it is critical to produce a receiver that is capable of
processing simultaneously a great number of these signals.
The main difficulty resides in the fact that the satellites are
able to broadcast signals with various frequencies and
bandwidths. For example the GPS L1 is 1575.42 MHz
with 2MHz of bandwidth while Galileo E6 is centred on
1278.75 MHz and occupies a bandwidth of 40 MHz. In
this particular situation, the only way to take advantage of
the GNSS satellites diversity is to design a receiver which
can process all the frequencies at the same time.
During several years, the GNSS front-end designers were
using the superheterodyne architecture to build up GNSS
receiver front-ends. Such solution has proved its
efficiency, simplicity and cost effectiveness for single-
frequency receivers. Today, as the GNSS receivers require
multifrequency front-end solutions, it became challenging
to produce a multifrequency receiver front-end that is
based on the superheterodyne architecture. Previous work
was conducted by several research teams, in which a
bandpass sampling-based receiver was proposed as an
alternative approach to building multifrequency receivers,
replacing the usual superheterodyne architecture. For
Figure 11: Dual-polarization GNSS user equipment architecture with tracking-domain multipath mitigation
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23rd International Technical Meeting of the Satellite Division of
The Institute of Navigation, Portland, OR, September 21-24, 2010
example, [29] has proposed an architecture of receiver
prototype to be used for communication application. In the
GNSS context, [30] was among of the firsts who devised a
working receiver prototype based on bandpass sampling.
On the other hand [31] and [32] work have pioneered the
idea of using a reconfigurable software-defined radio
(SDR) multifrequency receiver combined with a novel
architecture of digital-to-analogue converters.
In this paper, the ADVRG team at Westminster are
proving a front-end that is capable of processing the
maximum number of the channels with the minimum
complexity and high level of flexibility. The front-end
presented here, was achieved in context of the continuity
of the [31] and [32] studies. It is also slightly inspired by
the [33] work to provide a prototype of front-end ready for
applications such as multipath mitigation, pecise point
positioning (PPP) or integrity assessments.
FPGA Board
Virtex VI Xilinx
FPGA
Low jitter Clock
OCXO
CClk-
Clk+
PCIe
Data
Bus
ADC
DSP
GAIN Block
Filter/LNA Block
BPF BPF
Figure 12: Block-diagram of the receiver front-end.
Bandpass sampling is a well know signal processing
technique, often used in the military field. It started to be
used for GNSS when it was perceived as ideal for
multifrequency processing [30]− [34]. The technology
used is nowadays reasonably affordable and stable. The
key elements to build a bandpass sampling receiver are:
high speed analog-to-digital converter (ADC), low jitter
clock, selective filters and high gain amplifiers. Figure 12
represents the block-diagram of a bandpass sampling-
based front-end. At least, two selective filters are
necessary to cancel the out-band noise and get the receiver
operating properly. In the bandpass sampling receiver, the
RF signal is downconverted into the ADC by sub-
sampling techniques. In this case, the designer has to be
aware of few critical issues which can degrade the
sensitivity of the receiver.
The first issue is to prevent the noise to fold-back by sub-
sampling in the baseband. This is why selective filters are
used to minimise this effect. The filters used are based on
surface acoustic wave (SAW) technology or custom
microstrip filters such as those presented in [35]. The
second issue is to make sure that the different GNSS
channels are not overlapping. For this purpose, the ADC
sampling frequency is selected carefully by using the
ladder diagram presented in Figure 13 (see [30]). Finally,
the clock jitter must be as low as possible to not affect the
dynamic range of the sampled signals. In our front-end,
the master clock is derived from the combination of two
cascaded phase locked loops. The first one consists of a
narrow loop filter that cancels the low frequency noise and
the second one has a large loop filter for the high
frequency noise cancellation.
Figure 13: Ladder diagram
Figure 14 exhibits the front-end prototype designed
following the block-diagram of Figure 12. The two
aluminium boxes shown in Figure 14, contain the RF
filters and can be easily dismantled to replace them with
others and have the front-end working at different
frequencies. The front-end is interfaced with a field-
programmable gate array (FPGA) that performs some
basic post-processing and send the raw data towards the
acquisition and tracking systems whether they are
software or hardware-based devices.
Figure 14: Bandpass sampling front-end.
:
For this study, the ADVRG team is currently developing a
dual-input GNSS receiver that is capable of separately
tracking the GNSS signals from each output of the dual-
polarization antenna and then combining the
measurements by using a similar method to the one
described in Section 3.3. The dual-input receiver is based
on the same principle of band-pass sampling that is
presented in this paper. However some novelties were
introduced in the new design to avoid simple duplication
of components and reduce the complexity of the front-end.
The new design will take advantage of the simplicity and
the flexibility of the band-pass sampling technique,
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23rd International Technical Meeting of the Satellite Division of
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enabling it to be used for multipath mitigation techniques
that require dual-tracking receivers.
6. CONCLUSIONS AND FUTURE WORK
It has been experimentally verified that carrier power to
noise density, C/N0, measurements obtained by separately
correlating the RHCP and LHCP outputs of a dual-
polarization GNSS antenna can be used to distinguish
between a low-multipath and moderate-multipath
environment. On this basis, a dual-polarization antenna
may be used for multipath detection. However, the
sensitivity is much better for higher elevation satellites
than for lower elevation satellites. Further work will be
performed to improve the precision of the antenna
calibration and study the correlation between the RHCP-
LHCP C/N0 difference and the multipath errors.
Three different multipath mitigation techniques that use a
dual-polarization antenna have been proposed.
Measurement weighting estimates the code and carrier
multipath error standard deviation from the RHCP-LHCP
C/N0 difference and elevation angle. This is used by the
navigation processor to discard and reweight
measurements. Range-domain multipath correction, uses
the pseudo-range, carrier-phase and C/N0 differences
between the outputs of RHCP and LHCP receiver tracking
channels, together with antenna calibration data, to
estimate corrections to the code and carrier measurements.
In tracking-domain multipath mitigation, the RHCP and
LHCP correlator outputs are input to common acquisition
and tracking algorithms which attempt to separate the
direct line of sight and reflected signals. All three methods
will be developed and evaluated.
A novel dual-input GNSS front end has been designed,
based on direct RF sampling. This will be used, in
conjunction with a software GNSS receiver, for future
development and testing of multipath mitigation using a
dual-polarization antenna.
ACKNOWLEDGEMENTS
This work is part of the Innovative Navigation using new
GNSS Signals with Hybridised Technologies (INSIGHT)
program. INSIGHT (www.insight-gnss.org) is a
collaborative research project funded by the UK’s
Engineering and Physical Sciences Research Council
(EPSRC) to extend the applications and improve the
efficiency of positioning through the exploitation of new
global navigation satellite systems signals. It is being
undertaken by a consortium of twelve UK universities and
industrial groups: Imperial College London, University
College London, the University of Nottingham, the
University of Westminster, EADS Astrium, Nottingham
Scientific Ltd, Leica Geosystems, Ordnance Survey of
Great Britain, QinetiQ, STMicroelectronics, Thales
Research and Technology UK Limited, and the UK Civil
Aviation Authority.
The authors would like to thank Mojtaba Bahrami of UCL
for assisting with the conversion of the Leica data into a
format suitable for analysis.
REFERENCES
[1] Groves, P. D., Principles of GNSS, Inertial and
Multisensor Integrated Navigation Systems, Artech
House, January 2008.
[2] Braasch, M. S., “Multipath Effects,” In Global
Positioning System: Theory and Applications
Volume I, pp. 547–568, Parkinson, B. W. and
Spilker, J. J., Jr (eds), Washington, DC: AIAA, 1996.
[3] Van Nee, R. D. J., “GPS Multipath and Satellite
Interference,” Proc. ION 48th AM, Washington, DC,
June 1992, pp. 167–177.
[4] Brenneman, M., Morton, J., Yang, C. and van Graas,
F., “Mitigation of GPS Multipath Using Polarization
and Spatial Diversities,” Proc. ION GNSS, Fort
Worth, TX, September 2007, 1221-1229.
[5] Antcom, Vertical/Horizontal or RHCP/LHCP L1/L2
GPS Antennas: Pictures, Outline Drawings, and
Specifications, http://www.antcom.com/documents/
catalogs/RHCP-LHCP-V-H-L1L2GPSAntennas.pdf,
accessed 24 Feb 2010.
[6] Park, K., et al., “Multipath Characteristics of GPS
signals as determined from the Antenna and
Multipath Calibration System (AMCS),” Proc. ION
GPS, Portland OR, September 2002, pp. 2103−2110.
[7] Wanninger, L., and M. May, “Carrier Phase
Multipath Calibration of GPS Reference Stations,”
Proc.ION GPS 2000, Salt Lake City UT, September
2000, pp. 132−144.
[8] Lau, L., and P. Cross, “ Development and Testing of
a New Rigorous Ray Tracing Approach to GNSS
Carrier Phase Multipath Modelling,” Journal of
Geodesy, Vol. 81, No. 11, 2007, pp. 713−732.
[9] Reichert, A. K. and Axelrad, P., “Carrier-Phase
Multipath Corrections for GPS-based Satellite
Attitude Determination,” Navigation: JION, Vol. 48,
No. 2, 2001, pp. 77–88.
[10] Hodgart, S. and Wong, R., “Statistically Optimized
In-Flight Estimation of GPS Carrier Phase Multipath
for LEO Satellite Attitude Determination,”
Navigation: JION, Vol. 53, No. 3, 2006, pp. 181–
202.
[11] Thornberg, D. B., et al., “LAAS Integrated
Multipath-Limiting Antenna,” Navigation: JION,
Vol. 50, No. 2, 2003, pp. 117–130.
[12] Ray, J. K., Mitigation of GPS Code and Carrier
Phase Multipath Effects Using a Multi-Antenna
System. PhD Thesis, University of Calgary, 2000.
[13] Lau, L., and P. Cross, “Prospects for Phase
Multipath Mitigation Using Antenna Arrays for Very
150
23rd International Technical Meeting of the Satellite Division of
The Institute of Navigation, Portland, OR, September 21-24, 2010
High Precision Real-Time Kinematic Applications in
the Presence of New GNSS Signals,” Proc.
European Navigation Conference, Manchester, UK,
May 2006.
[14] Van Dierendonck, A. J., P. Fenton, and T. Ford,
“Theory and Performance of a Narrow Correlator
Spacing in a GPS Receiver,” Navigation: Journal of
the ION, Vol. 39, No. 3, 1992, pp. 265–283.
[15] Bétaille, D., P. A. Cross, and H.-J. Euler,
“Assessment and improvement of the capabilities of
a window correlator to model GPS multipath phase
errors,” IEEE Transactions on Aerospace and
Electronic Systems, Vol. 42, No. 2, 2006, pp.
707−718.
[16] Fenton, P. C., and J. Jones, “The Theory and
Performance of NovAtel Inc.’s Vision Correlator,”
Proc. ION GNSS 2005, .Long Beach, CA, September
2005, pp. 2178−2186.
[17] Pratt, A. R., “Performance of Multi-path Mitigation
Techniques at Low Signal to Noise Ratios,” Proc.
ION GNSS 2004. Long Beach, CA, September 2004,
pp. 43–53.
[18] Hartinger, H., and F. K. Brunner, “Variances of GPS
Phase Observations: The SIGMA-ε Model,” GPS
Solutions, Vol. 2, No. 4, 1999, pp. 35−43.
[19] Wieser, A., and F. K. Brunner, “An extended weight
model for GPS phase observations,” Earth Planets
Space, Vol. 52, 2000, pp. 777−782.
[20] Wieser, A., and F. K. Brunner, “SIGMA-F:
Variances of GPS Observations Determined by a
Fuzzy System,” Proc. International Association of
Geodesy Symposia 125: Vistas for Geodesy in the
New Millennium, Springer Verlag, 2002, pp.
365−370.
[21] Lau, L., and E. Mok, “Improvement of GPS Relative
Positioning Accuracy By Using SNR,” Journal of
Surveying Engineering, Vol. 125, No. 4, 1999, pp.
185−202.
[22] Lau, L., and P. Cross, “A New Signal-to-Noise-Ratio
Based Stochastic Model for GNSS High-Precision
Carrier Phase Data Processing Algorithms in the
Presence of Multipath Errors,” Proc. ION GNSS
2006, Fort Worth, Texas, September 2006, pp.
26−29.
[23] Comp, C. J., and P. Axelrad, “An Adaptive SNR-
based Carrier Phase Multipath Mitigation
Technique,” Proc. ION GPS-96, Kansas City, MO,
September 1996, pp. 683−697.
[24] Lau, L., and P. Cross, “Use of Signal-To-Noise Ratio
for Real-Time GNSS Phase Multipath Mitigation,”
Proc. NAV05, London, UK, November 2005.
[25] Manandhar, D., Shibasaki, R. and Normark P., 2004.
GPS Signal Analysis using LHCP/RHCP Antenna
and Software GPS Receiver, Proc. ION GNSS, Long
Beach, CA, September 2004, pp. 2480−2498.
[26] Yang, C., and A. Porter, “GPS Multipath Estimation
and Mitigation Via Polarization Sensing Diversity:
Parallel Iterative Cross Cancellation,” Proc. ION
GNSS, Long Beach, CA, September 2005, pp.2707-
2719.
[27] A Izadpanah, Parameterization of GPS L1 Multipath
Using a Dual Polarized RHCP/LHCP Antenna, MSc
Thesis and UCGE Report no. 20280, University of
Calgary, 2009.
[28] Groves, P. D., “GPS signal to noise measurement in
weak signal and high interference envrironments,”
Navigation: JION, Vol. 52, No. 2, 2005, pp. 83–92.
[29] H. Kim, J. Kim, JH. Kim, H. Wang, and I. Lee, “RF
Band-Pass Sampling Frontend for Multiband Access
CR/SDR Receiver”, ETRI Journal, vol.32, no.2, Apr.
2010, pp.214-221.
[30] Akos, D.M.; Stockmaster, M.; Tsui, J.B.Y.;
Caschera, J., “Direct bandpass sampling of multiple
distinct RF signals”, IEEE Trans. on
Communication, Vol. 47, No. 7, 1999.
[31] A. Ucar, E. Cetin and I. Kale, “Reconfigurable SDR
Receiver for Multi-Mode GNSS Applications”,
Proceedings of the SDR ’08 Technical Conference
and product Exposition, 2008.
[32] Ucar, A.; Cetin, E.; Kale, “On the implications of
Analog-to-Digital Conversion on variable-rate
BandPass Sampling GNSS receivers”, Signals,
Systems and Computers, 2008 42nd Asilomar
Conference, Oct. 2008.
[33] Lamontagne, G. « Conception électrique d'un
échantillonneur direct en bande L des signaux des
GNSS ». Rapport technique de projet sythèse en
génie électrique, École de technologie supérieure,
2007.
[34] Adane, Yacine and Kale, Izzet, “RF front-end design
for full constellation SDR based GNSS receivers,”
IGNSS 2009, Book of abstracts p. 38.
[35] Y. Adane, A. Constantinescu, J. Belzile, A. Kouki,
"Design of Miniature RF Band-pass Filters for a
Hybrid GPS/Galileo Receiver Front-End Insert",
ION NTM 2007, 22-24 January, San Diego, CA,
p350-356.
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23rd International Technical Meeting of the Satellite Division of
The Institute of Navigation, Portland, OR, September 21-24, 2010
