ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 1/14
A Standalone Approach for Ultra-Tightly
Coupled High Sensitivity GNSS Receiver
Tiantong Ren and Mark G. Petovello
Position, Location And Navigation (PLAN) Group
Department of Geomatics Engineering
Schulich School of Engineering
University of Calgary
Chaminda Basnayake
General Motors
Warren, MI USA
BIOGRAPHIES
Tiantong Ren is a Ph.D. candidate in the Position,
Location And Navigation (PLAN) group in the
Department of Geomatics Engineering at the University
of Calgary. In 2010 he completed a II Level Specializing
Master on Navigation and Related Applications in
Politecnico di Torino, Italy. In 2008 he completed a M.Sc.
on Electronic Engineering in Beihang University, China.
His research interest is GNSS signal processing and high-
sensitivity GNSS receiver design in signal challenged
environments.
Dr. Mark Petovello is a professor in the Position,
Location And Navigation (PLAN) group in the
Department of Geomatics Engineering at the University
of Calgary. He has been actively involved in the
navigation community for over 15 years and has received
several awards for his work. His current research focuses
on software-based GNSS receiver development and
integration of GNSS with a variety of other sensors.
Dr. Chaminda Basnayake is a Senior Research Engineer
at General Motors R&D and Planning where he is leading
the GNSS-based vehicle navigation technology R&D
efforts. His current research focuses on enabling
ubiquitous positioning capability in land vehicles and
using such capabilities in next generation automobile
systems including communications-enabled applications.
ABSTRACT
The tracking threshold in conventional scalar-based
GNSS receivers limits the performance of ML bit
decoding, which is paramount to ML based bit wipe-off.
In this paper, a standalone approach, that is, in the
absence of aiding information, is proposed for the ultra-
tightly coupled high sensitivity GNSS receiver. The
benefits are analyzed and determined of using ultra-
tightly coupled GNSS receiver in the standalone mode to
improve bit decoding and navigation solutions.
The results show the ultra-tight receiver is more robust
than the GNSS-only receivers. In the context of GPS L1
C/A signals, the field test results show ultra-tight receiver
can improve the successful decoding rate (SDR)
compared to scalar-based receiver. Compared to the
vector-based receiver, the ultra-tight receiver is more
immune to the high BER problem in extending coherent
integration time. The position and velocity accuracy of
the standalone ultra-tight receiver has been improved
more than 40% after extending coherent integration time
in the vehicular navigation test in urban canyon. A newly
proposed signal power based update observation selection
strategy has helped to mitigate multipath impacts.
INTRODUCTION
Global Navigation Satellite Systems (GNSS) such as the
Global Positioning System (GPS) can provide users with
accurate navigation and timing services worldwide. They
are vital for applications such as aircraft auto-piloting,
automobile en-route guidance, pedestrian positioning, etc.
Recently, processing weak GNSS signals has been
receiving growing attention because of the increased
demand for navigation in indoors, under dense foliage
canopies and in urban canyons.
High-sensitivity GNSS receivers are capable of providing
satellite measurements for signals attenuated by up to
about 30 dB (Media Tek 2012, Fastrax 2012 and u-blox
2011). For high-sensitivity GNSS receivers, extending
integration time coherently is optimal for obtaining higher
sensitivity, mitigating multipath and cross-correlation
false locks, and avoiding squaring loss. However, longer
coherent integration time is primarily limited by the
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 2/14
navigation message data bit, if present. For coherent
integration beyond the data bit period, navigation data bit
wipe-off is required to avoid energy loss that occurs due
to bit transitions. Furthermore, complete bit wipe-off
requires the knowledge of bit boundaries and bit values.
The process of determining the location of the bit
boundaries and extracting the bit values is herein called
bit synchronization and bit decoding respectively. Bit
synchronization only needs to be achieved once at the
beginning of data processing. However bit wipe-off
requires bit values to be decoded continuously. This paper
focuses on the bit decoding analysis, and bit
synchronization algorithms can be referred to Ren et al
(2012).
By using the navigation data bit aiding and frequency
aiding from an external source, Akos et al (2000) showed
that during acquisition, signals with carrier to noise-
density ratios (C/N0) of 32, 22, 17, and 12 dB-Hz can be
detected if the coherent integration time is at least 8, 200,
400, and 800 ms respectively. Similarly Van Diggelen &
Abraham (2001), Djuknic & Richton (2001) and Di
Esposti (2007) used aiding information from wireless
network broadcasting to improve sensitivity. However, all
of these methods need access to external aiding sources,
and the receiver will correspondingly lose its autonomy
which may not be possible or desirable in all applications.
For extracting the bit values without an external aiding
source, Maximum-Likelihood (ML) algorithms (i.e., ML
bit decoding) introduced in Soloviev et al (2009), have
been shown to outperform other algorithms for weak
GNSS signals. However, the performance of ML bit
decoding relies heavily on the signal tracking
performance (i.e., tracking threshold). Gleason & Gebre-
Egziabher (2009) mentioned that a Costas phase-locked
loop (PLL) had difficulties following the signal at about
35 dB-Hz, below which the process of bit decoding
cannot be performed. In Ren et al (2013), the performance
of ML bit decoding algorithm was shown to be improved
by using a vector-based GNSS receiver. This paper looks
at whether the performance of ML bit decoding and,
subsequently, bit wipe-off, can be further improved in an
ultra-tightly coupled GNSS receiver.
An ultra-tightly coupled GNSS receiver is a system that
integrates GNSS measurements with inertial and/or other
sensor outputs, and uses the integrated estimate of
position and velocity to update the GNSS channel
estimates of the Doppler frequency and code phase.
Generally, compared to a GNSS-only solution, the
advantages of integrating GNSS and inertial sensors
include improved accuracy, smoother trajectories,
availability of an attitude solution, reduced susceptibility
to interference and increased sensitivity (Soloviev et al
2004, Petovello & Lachapelle 2006, and Petovello et al
2008). Specifically, Soloviev et al (2004) demonstrated
the reacquisition and continuous carrier phase tracking of
15 dB-Hz GPS signals in flight test with simulated noise;
Petovello & Lachapelle (2006) showed an ultra-tightly
coupled GPS and inertial navigation system (INS)
architecture is able to track the carrier phase under foliage
up to an attenuation of about 15 dB and still maintains a
velocity solution accurate to a few centimeters per
second. Petovello et al (2008) demonstrated that the ultra-
tight receiver provided about 7 dB of sensitivity
improvement over the standard receivers.
INS has two prevalent dead reckoning (DR) models,
namely the traditional INS mechanizations (e.g., Soloviev
et al 2004, Petovello & Lachapelle 2006, and Petovello et
al 2008), and the vehicle sensor based DR algorithm (e.g., Fouque et al 2008, and Li 2012). The former approach
obtains velocities by integrating accelerometer outputs,
and position increments by integrating the velocities. The
latter DR algorithm – usually used in land vehicle
navigation – directly uses wheel speed sensor information
to obtain the vehicle’s velocity. The main benefit of DR
algorithm over the traditional INS mechanization is the
accuracy of DR algorithm degrades with the travelled
distance rather than with time. All the algorithms
implemented in this paper are based the ultra-tightly
coupled system of GNSS and DR algorithm. Although
different models of vehicles may be equipped with
different vehicle sensor setups, the ultra-tightly coupled
system in this work employs a general configuration,
which contains four wheel speed sensors (two in front and
two in rear), a steering angle sensor, a longitudinal
accelerometer and a vertical gyroscope (i.e., yaw rate
sensor) (WSS/SAS/1A1G). This is a reduced order low-
cost Micro Electro-Mechanical Systems (MEMS)-based
inertial measurement unit (IMU) (1A1G) with WSS and
SAS.
The objective of this paper is to determine the benefits of
using an ultra-tightly coupled GNSS receiver in
standalone mode to improve bit decoding. Furthermore,
the paper uses the estimated data bits to extend coherent
integration using bit wipe-off and then assesses the
accuracy of the resulting navigation solution, and finally
determines the feasibility of the standalone approach.
The contributions of this paper are threefold. First, it
assesses the performance of ML bit decoding in the ultra-
tight system. Second, it assesses the navigation
performance with extended coherent integration time after
bit wipe-off. Third, it gives a strategy for mitigating
multipath impacts in a software-based ultra-tightly
coupled GNSS receiver in signal challenged
environments.
The paper begins by describing the ML bit decoding
algorithm and the vehicle sensor configuration. Next the
ultra-tight system and different architectures of GNSS
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 3/14
receivers used in this paper are introduced. Then a signal
power based strategy is provided for mitigating multipath
impacts. Later the field test performed in this work is
described. Finally the test results are presented and
analyzed.
In the context of this work, the performance of bit
decoding is assessed in terms of the successful decoding
rate (SDR) of bit values. The proposed algorithms are
derived for a generic binary phase shift keying (BPSK)
GNSS signal, but are assessed using GPS L1 C/A signals
only.
SIGNAL AND SYSTEM MODEL
This section gives a brief overview of the ML bit
decoding algorithm used in this paper.
Signal Model
The GNSS signal transmitted through an additive white
Gaussian noise (AWGN) channel and received at the
antenna of a GNSS receiver in the radio frequency (RF)
band is represented by
,
1
( ) ( ) ( )svN
RF RF i RF
i
y t r t n t
(1)
Specifically, it is the sum of svN line-of-sight (LOS)
signals (, ( )RF ir t ) from svN satellites in view, plus a noise
term ( )RFn t . In general, the Signal in Space (SIS) at the
input of a GNSS receiver from the thi satellite has the
following structure
,
, ,
( ) ( ) ( )
cos 2
RF i i i i i i
RF d i RF i
r t Ab t c t
f f t
(2)
where iA is the amplitude of the signal; ( )i ib t is the
navigation message where each binary unit is called a bit;
( )i ic t is the ranging code; i is the time delay
introduced by the transmission channel; RFf is the GNSS
carrier frequency; ,d if is the Doppler frequency shift and
,RF i is the initial carrier phase offset.
After being down-converted in the receiver’s front-end,
signals from different satellites are approximately
orthogonal so that the i subscript can be dropped and
each signal can be written separately as
( ) ( ) ( )
( ) ( )cos 2 ( )IF d
y t r t n t
Ab t c t f f t n t
(3)
where IFf is the nominal intermediate frequency (IF) of
the down-converted signal.
At this stage, the purpose of a GNSS receiver is to
estimate and df , thus allowing for the determination
of the pseudorange and pseudorange range to each
satellite, which is then used to calculate the receiver’s
position, velocity and time (PVT) parameters. To
accomplish this, each and df is estimated from a cross
ambiguity function (CAF), which is the cross-correlation
between the received signal and the locally generated
signal, and is given by
0
0
( , ) ( ) ( )
( ) ( )cos 2
b
b
T
d
t
T
IF d
t
R f y t r t
y t c t f f t
(4)
where ( )r t is the locally generated signal; , df and
are the corresponding locally-generated signal parameters
used to generate ( )r t ; bT is the data bit period, which is
herein assumed to equal the coherent integration time for
cross-correlation. The coherent integration should
accumulate between bit boundaries. Usually bit boundary
locations are detected by a bit synchronization process,
and herein the bit synchronization is assumed to have
been correctly completed. The form of ( , )dR f can be
different if using non-coherent integration though it is not
addressed in this paper. Finally, the ML estimate of
and df is given by
,
ˆˆ, arg max ( , )d
d df
f R f
(5)
Bit sign transition affects the CAF evaluation especially
when the coherent integration time is longer than the bit
period ( bT ). In particular, bit sign transitions modify the
shape of the CAF envelope and may divide the central
peak of CAF in frequency domain into two split side
lobes (Sun & Lo Presti 2010; Jeon et al 2011).
ML Bit Decoding Algorithm
As input, the ML bit decoding algorithm uses several
cross-correlation outputs, each computed using a coherent
integration time equal to the data bit period. After
obtaining the ML estimate of and df , the locally
generated signal and relative parameters can be
represented as ˆ( ) ( )r t r t , ˆ and ˆd df f . If there is
an error in and ˆdf compared to incoming values, i.e.,
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 4/14
and ˆd d df f f , the thk cross-correlation
output of bT is given by
( 1)
, ,
,
ˆˆ ˆ( , ) ( ) ( )
( )sinc( )
exp 2
b
b
kT
k d
t k T
R k C k d b
d b I Q
R f y t r t
A R f T
j f kT n
(6)
which is the cross-correlation output from the signal
period of ( 1) bk T to bkT . Since the right hand side of the
above equation is only a function of and df , we
adopt the following shorthand notation to make this more
explicit
ˆˆ( , ) ( , ) ( , )k d k d kR f R f f R f (7)
,R kA is the amplitude of the cross-correlation output;
, ( )C kR is the normalized ranging code correlation
function with the code phase error of , which results
in a triangle shape attenuation in the cross-correlation
amplitude; df is the Doppler frequency error, which
results in a sinc-shaped attenuation in the cross-correlation amplitude and a rotating carrier phase, and;
is the initial carrier phase error.
Bit decoding is the process of determining data bit values
modulated on carrier. The likelihood function used in the
ML bit decoding algorithm is the inner product between a
cross-correlation output vector starting from a bit
boundary (so as to avoid integrating over a boundary) and
locally generated bit combinations. The cross-correlation
output vector with N bits is given by
1 2
( , )
( , ), ( , ),..., ( , )
d
d d N d
f
R f R f R f
NR (8)
If trying to decode N bits at a time, the number of
possible bit combinations is equal to 12N, and the correct
bit combination is supposed to have the maximum energy.
It is noted that the energy based ML bit decoding method
detects the bit transitions, not the actual bit values (i.e.,
there is a sign ambiguity), but this is sufficient for bit
wipe-off in order to extend coherent integration time.
The bit value combination matrix B ( 12N N ) is defined
as
1 1 ... 1
1 1 ... 1
... ... ... ...
1 1 ... 1
B (9)
For an N bit sequence, the inner product between
( , )df NR and the vector mb from the -thm row of B
is given by
1( , ) ( , ) ( 1,2,...2 )N
m d dI f f m N mR b (10)
The ML estimate of bit values can be found by
maximizing the energy of the inner product.
Mathematically, this is given as
2
[ 1,..., 1]
ˆ arg max , ;m dI f
m
mb
b b (11)
Vehicle Sensor Configuration
The DR algorithm uses the vehicle sensors’ output as
measurements to update the system rather than the
mechanization approach used in conventional INS. As
mentioned before, the ultra-tightly coupled system in this
paper employs four wheel speed sensors, a steering angle
sensor, a longitudinal accelerometer and a vertical
gyroscope (WSS/SAS/1A1G). This setup is similar to the
one used in Li (2012) and the system model is given by
0
0
0
n n
b b
y y
b bay y
Ay y
GG G
Ss s
V V
wa a
w
w
w
wb b
wd d
w
W
r r
F
S S
(12)
where nr is the position error vector in navigation frame
(i.e., local level frame or East-North-Up (ENU) frame); b
yV is the longitudinal velocity error in body frame; b
ya
is the longitudinal acceleration error in body frame,
modeled as random walk with the driving noise aw ;
is the pitch error, modeled as first order Gauss-Markov
process with the driving noise w ; is the azimuth
error; is the yaw rate error, modeled as random walk
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 5/14
with the driving noise w ; S is the scale factor error
vector of wheel speed sensors, modeled as first order
Gauss-Markov process with the driving noise wW ; yb is
the longitudinal accelerometer bias, modeled as first order
Gauss-Markov process with the driving noise Aw ; Gd is
the gyroscope drift, modeled as first order Gauss-Markov
process with the driving noise Gw ; and s is the
steering angle error, modeled as first order Gauss-Markov
process with the driving noise Sw . Besides, F is the
dynamics matrix and given by
1 2 3
2
0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
sin 10 0 0 0 0 0 0 0
coscos
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
W
A
G
S
R R R
F
(13)
where x (e.g., when x represents ) is the
reciprocal of the time constant in each first order Gauss-
Markov process. In addition, 1R , 2R and 3R are the
columns of the fractional dynamics matrix R , and the
fractional system model relevant to nr is given by
3 2
sin cos cos cos sin sin
cos cos sin cos cos sin
sin 0 cos
E
n
N
U
b
y
b
y
b b b
y y y
b b
y y
b
y
r
r
r
V
V
V V V
V V
V
1
r
R
R R R
(14)
GNSS RECEIVERS AND THE STANDALONE
APPROACH
The above algorithms have been assessed in a software-
based GNSS receiver platform called GSNRxTM
, which is
developed in C++ by the PLAN Group at the University
of Calgary. This section describes the different versions
of the software used for processing.
There are three GNSS receiver architectures used in this
work. The first, shown in Figure 1, is the standard (i.e.,
scalar-based) GNSS receiver, which contains independent
local tracking loops/channels dedicated to providing
independent pseudorange and/or pseudorange rate
measurements for PVT calculations.
Figure 1 - Architecture of a standard GNSS receiver
The second architecture is shown in Figure 2 and is a
vector-based high-sensitivity GNSS receiver called
GSNRx-hs™. The difference between a scalar-based
GNSS receiver and a vector-based GNSS receiver is that
the latter sets the numerically controlled oscillators
(NCOs) – inside the local signal generator – from
navigation filter outputs rather than from local channel
filter outputs. A vector-based GNSS receiver requires
knowledge about ephemeris data when updating the
channel parameters. To this end, long term ephemeris
(e.g., Garrison and Eichel 2006) could be used in order to
remain consistent with the motivation of maintaining as
much independence of the receiver as possible from
external data sources. In addition, the GSNRx-hsTM
contains an open-loop tracking structure, which can avoid
the stability problem (Ward et al 2006) caused by long
coherent integration in normal closed-loop tracking. This
is preferred for building the correlator output
measurements with long coherent integration. The
detailed benefits of open-loop tracking can be found in
van Graas et al (2005). This architecture omits the carrier
tracking because it is usually vulnerable in signal
challenged environments.
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 6/14
Figure 2 - Architecture of a conventional GSNRx-hs
TM
receiver with external bit aiding
The third architecture shown in Figure 3 is an ultra-tightly
coupled high sensitivity GNSS receiver, called GSNRx-
hs-dr™. Except for the integrated vehicle sensors and DR
based navigation filter, the GNSS receiver part is similar
to GSNRx-hsTM
. However, the receiver was also modified
for this work to uses ML bit decoding to extend
integration time, as shown in Figure 4. This modified
version can be considered as a standalone ultra-tightly
coupled high-sensitivity GNSS receiver.
Figure 3 - Architecture of a conventional GSNRx-hs-
drTM
receiver with external bit aiding
Figure 4 - Architecture of a new GSNRx-hs-drTM
receiver with ML bit decoding
POWER-BASED OBSERVATION SELECTION
STRATEGY FOR MITIGATING MULTIPATH
In the urban canyon environment, due to massive
reflecting surfaces from buildings and skyscrapers,
multipath can be considered as the most destructive error
source. In order to mitigate multipath impacts, this work
proposes a signal power – as determined by the carrier to
noise-density (C/N0) – based observation selection
strategy for selecting suitable GNSS pseudorange and
Doppler measurements in the navigation filer.
A signal power threshold can be used to mitigate
multipath. From Xie (2013), reflected signal rarely
exceeds 42 dB-Hz in such environments. As such, if a
satellite’s C/N0 is higher than this, all of its measurements
are used. Otherwise, only the Doppler measurement is
used. Although signal power threshold chosen here is 42
dB-Hz, it would ideally be an environment-dependent
value.
The reason for only using Doppler measurement in a LOS
and non-line-of-sight (NLOS) mixed signal channel is
twofold. First, the correct Doppler measurement can be
extracted in the open-loop tracking if the LOS signal and
the NLOS signals can be separated in frequency domain
(Xie & Petovello 2011); the Doppler error caused by
NLOS signal is limited if the LOS signal cannot be
separated from NLOS signals. In other words, the
Doppler bias from NLOS signals should be small -
especially with extended coherent integration time - if
LOS signal and NLOS signals are overlapped in
frequency domain. Second, the vehicle sensors can
correct the system velocity with a higher update rate if the
Doppler update (i.e., velocity update) from GNSS signals
is incorrect. However, in this case, there is no direct
position update from vehicle sensors or DR algorithm,
and any error in position will remain until the next
accurate GNSS update. In other words, the ultra-tight
receiver is more vulnerable to pseudorange errors than
Doppler errors.
This work will assess the performance of this strategy in
the ultra-tight coupled high-sensitivity receiver in urban
canyon test. This method relies on C/N0 estimate
algorithms, and in this work, the C/N0 estimation was
implemented by separately calculating the signal power
and noise power spectral density and then computing their
ratio. The open-loop tracking that can distinguish a LOS
signal and NLOS signals in frequency domain also
mitigates the multipath fading in C/N0 estimation.
A potential problem that needs to be considered is if the
signal power is low, the bit error rate (BER, which equals
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 7/14
to “1 - SDR”) of ML bit decoding will increase when
extending integration time. Specifically, bit wipe-off with
non-zero BER will attenuate the power of correlator
outputs, and a high BER may split the peak in the
frequency domain. Ren et al (2013) has proposed two
methods that select suitable correlator outputs for input
into the ML bit decoding algorithm. The methods have
been shown effective for a GNSS only solution in the
signal challenged dense foliage environment. This work
will assess the necessity of such methods for an ultra-tight
coupled high-sensitivity receiver in the urban canyon test.
TEST DESCRIPTION
First, Monte Carlo simulations based on the signal model
in (6) with 10,000 trials are performed to generate the
ideal results for comparison. The Monte Carlo simulations
assume no tracking errors in the correlator outputs (i.e.,
0, 0df ), and that the bit transition happens with
a probability equals 50%. The Monte Carlo simulation
results are treated as the upper bound of ML bit decoding.
Second, in order to assess the performance of ML bit
decoding in the ultra-tight receiver in real environments, a
vehicular field test was used. In this case, only the GPS
L1 C/A code signal was processed.
The vehicular field test was conducted in a signal
challenged environment, namely in urban canyon in the
downtown of Calgary. An overlooking view of Calgary
downtown is shown in Figure 5 and images captured
along the test trajectory are shown in Figure 6. As can be
seen, there are portions of the test where sky visibility is
highly restricted. The ultra-tightly coupled GSNRx-hs-
drTM
, the vector-based GSNRx-hsTM
and the scalar-based
GSNRxTM
were used for processing the field data. The
C/N0 values estimated by GSNRx-hs-drTM
of all satellites
in view in the urban canyon test are shown in Figure 7.
As shown, the signal power values fluctuate markedly (a
cumulative histogram is also shown in Figure 12). Some
attenuations of C/N0 are higher than 25 dB.
In order to compute SDR values, reference data bits (i.e.,
the true data bits) are obtained from an antenna located on
the roof of the CCIT building at the University of
Calgary, such that the BER of signals is negligible (C/N0
of around 45 dB-Hz).
Figure 5 - Calgary downtown area in an overlooking
view (A panoramic shot of downtown Calgary from
the Calgary Tower, Architecture Calgary, Foundation
3D Forums, Web. 1 Mar 2012
<http://www.foundation3d.com/forums/showthread.ph
p?p=222872 >)
Figure 6 – Urban canyon environment in the field test
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 8/14
Figure 7 - C/N0 of all satellites in view in the urban
canyon test estimated by GSNRx-hs-drTM
A reference system consisting of a NovAtel SPAN SE™
unit (which contains a NovAtel OEMV receiver and an
LCI tactical-grade inertial measurement unit (IMU)) was
used to provide the reference trajectory. The estimated 1
accuracy of the reference system (per axis) is 0.2 m for
position and 0.02 m/s for velocity. The land vehicle with a
NovAtel GPS-702-GG antenna and a LCI IMU on top
and a National Instruments PXI-5600 front-end inside is
shown in Figure 8.
Figure 8 - The land vehicle with navigation systems for
field data collection. The upper-left picture shows a
NovAtel GPS-702-GG antenna and a LCI IMU on top
of the vehicle. The upper-right picture shows a
National Instruments PXI-5600 front-end inside the
vehicle. The lower picture shows the land vehicle.
In both tests, the IF data were collected using a National
Instruments PXI-5600 front-end which includes an oven
controlled crystal oscillator (OCXO). The front-end
parameters are shown in Table 1. The front-end has a 16
bit quantization level, although for the data collected
typically only 3-4 bits are necessary (i.e., are non-zero).
The data were processed by and all algorithms were
implemented in the GSNRxTM
software receiver suites as
introduced earlier.
Table 1 - Front-end parameters used for collecting
GNSS data in dense foliage test
Parameter Value (MHz)
Intermediate Frequency 0.42
Sampling Rate (I/Q) 10.0
Bandwidth 5.0
The built-in vehicle sensors – including wheel speed
sensors, inertial sensors and steering angle sensors –
originally equipped in the vehicle to improve the safety
and operational stability, are used in this work to build the
DR system. Wheel speed sensors are used as a low cost
solution to measure vehicle’s speed. A steering angle
sensor is used to measure the wheel turning angle with
respect to the neutral position (Li 2012). With the steering
angle sensor, the front wheel speed sensors can provide
the in-track velocity and yaw rate estimation. The built-in
reduced MEMS IMUs including a longitudinal
accelerometer and a vertical gyroscope (1A1G) are used
to measure acceleration and angular velocity. The in-run
variability of the gyroscope is 113 deg/hr and the angular
random walk is 1044 deg/hr/ Hz .
RESULTS AND ANALYSIS
The analysis proceeds in three steps; first the bit decoding
is assessed; second, receiver performance is evaluated
using known data bits; and, the analysis is repeated using
data bits obtained from the bit decoding process within
the receiver.
Bit Decoding Performance in Ultra-Tight Receiver
First of all, a comparison of estimated C/N0 from the
scalar-based receiver and the ultra-tight receiver is shown
in Figure 9. PRN 25 is selected for comparison purpose
but the results are typical for other satellites. It is noticed
that the signal can be tracked in the ultra-tight receiver
although the signal power is quite low (between about 10
and 30 dB-Hz). In scalar-based receiver, however, during
more than half of time the signal is not tracked (indicated
by a C/N0 value equal to zero).
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 9/14
Figure 9 - C/N0 of PRN 25 in scalar-based receiver and
ultra-tight receiver in the urban canyon test
The SDRs of ML bit decoding as a function of signal
strength in the scalar-based receiver and the ultra-tight
receiver are shown in Figure 10. The number of bits to be
decoded here is two (i.e., N = 2), which is the optimal
number for tolerating Doppler errors (Ren et al 2012). For
every 40 ms of data (approximately from hundreds to ten
thousands samples in all), the bits were decoded and
compared to the known data bits; the SDR is then the
percentage of time the decoding was correct. The
statistics of bit decoding as a function of C/N0 are
calculated using bins with a width of ±2.5 dB.
Compared to the results in the scalar-based receiver, a
marked performance improvement of ML bit decoding in
the ultra-tight receiver is noticed. This shows the benefits
of the ultra-tight receiver for ML bit decoding, which
improves the SDR by 1% – 30% depending on the signal
strength. Compared with the Monte Carlo results, the
difference in SDR between the ultra-tight receiver and
Monte Carlo simulations is due to the non-white noise
factor present in field tests, e.g., due to multipath, as well
as the inherent tracking errors that are present within the
receiver.
Figure 10 - Performance of ML bit decoding as a
function of signal strength in scalar-based receiver
and ultra-tight receiver in the urban canyon test
Figure 11 shows the SDR from each satellite during the
entire field test. Compared to scalar tracking, the
improvement of the SDR resulting from the ultra-tight
receiver ranges from 2% to 30%. Figure 12 shows the
cumulative C/N0 plots of all satellites in the urban canyon
test, and, when cross-referenced against Figure 11 it is
noted that ultra-tight receiver improves the ML bit
decoding of weak signals more significantly.
An interesting phenomenon can also be viewed in Figure
11. Specifically, compared to the vector-based receiver,
the SDR from the ultra-tight receiver only has a slight
improvement. This suggests in the urban canyon test the
SDR cannot be further improved if the signal is blocked
by skyscrapers, even if the navigation solution has been
improved in the ultra-tight receiver. The results of the
navigation solution will be discussed later.
Figure 11 - Performance of ML bit decoding in scalar-
based receiver, vector-based receiver and ultra-tight
receiver in the urban canyon test
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 10/14
Figure 12 - Cumulative C/N0 plots of all satellites in
the urban canyon test
The SDR results in Figure 11 were obtained by setting the
open-loop search space uncertainty as 40 meters (for
pseudorange errors) by 10 Hz (for Doppler errors). This is
an empirical setting, and preferred when processing
GNSS signals in signal challenged environments. Figure
13 shows the SDR resulting from the ultra-tight receiver
with different search space sizes. The result shows that
the SDR has no obvious change with different search
space settings. This confirms the former conclusion that
the SDR cannot be further improved if the signal is
blocked.
Figure 13 - Performance of ML bit decoding with
different search space in ultra-tight receiver in the
urban canyon test
Navigation Results in Ultra-Tight Receiver with
External Bit Aiding
After estimating the data bits, bit wipe-off can be
performed in order to extend the coherent integration
time. However, before evaluating performance of such an
approach, the performance of DR only solution, GNSS
only solution and ultra-tightly coupled system are
assessed first.
The trajectory of DR only and the GNSS-only vector-
based solution are compared to the reference trajectory in
Figure 14. The coherent integration time in the vector-
based receiver is 100 ms using the external bit aiding
method (i.e., perfect bit information). The external bit
aiding method contains no bit errors, so it is expected that
it gives the best results. The trajectory of DR only
solution (green line in Figure 14) looks smooth but has
the accumulated position errors; the GNSS only solution
(red line in Figure 14) has no accumulated bias but is
noisy due to signal blockage and heavy multipath in the
urban canyon.
Figure 14 - Trajectory results of vector-based receiver
and DR only solution in the urban canyon test. The
blue line is the reference trajectory; the red line is the
result from vector-based receiver; and the green line is
the result of DR only solution. The coherent
integration time is 100 ms by external bit aiding.
The red line in Figure 15 shows trajectory result of the
ultra-tight receiver by using all pseudorange and Doppler
measurements. By ultra-tightly integrating the vehicle
sensors with the vector-based GNSS receiver, the
navigation solution has no obvious bias and is less noisy
than the GNSS-only solution. In addition, the “spike” in
the yellow circle in Figure 14 has disappeared. However,
in some area, large position errors are observable due to
multipath. So it is necessary to use the signal power based
observation selection strategy for mitigating multipath
impacts. The green line in Figure 15 shows the trajectory
result of the ultra-tight receiver by using this strategy, and
it is clear that a more accurate and smoother trajectory is
obtained.
Figure 15 - Trajectory results of ultra-tight receiver in
the urban canyon test. The blue line is the reference
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 11/14
trajectory; the red line is the result using all
measurements; and the green line is the result using
all measurements if C/N0 is larger than 42 dB-Hz,
otherwise using Doppler measurements only. The
coherent integration time is 100 ms by external bit
aiding.
Table 2 shows the statistics of the solutions shown above.
It is noted that the ultra-tight receiver with the signal
power based observation selection strategy outperforms
the other systems or settings. As such, the results from the
ultra-tight receiver that follow are based on this approach.
Table 2 – RMS position and velocity errors in DR only
solution, vector-based receiver, ultra-tight receiver
and ultra-tight receiver with new update strategy. The
coherent integration time is 100 ms by external bit
aiding.
RMS Position Error [m]
System North East Up
Vector-Based Receiver 14.3 6.4 64.9
DR Only 96.1 30.7 6.6
Ultra-Tight Receiver 12.3 3 46
Ultra-Tight Receiver with
the Signal Power Based
Observation Selection
Strategy
4 1.3 9.5
RMS Velocity Error [m/s]
System North East Up
Vector-Based Receiver 0.42 0.23 0.36
DR Only 0.59 0.16 0.05
Ultra-Tight Receiver 0.13 0.07 0.11
Ultra-Tight Receiver with
the Signal Power Based
Observation Selection
Strategy
0.11 0.08 0.09
Navigation Results in Ultra-Tight Receiver with ML
Bit Decoding
After assessing the performance of different systems with
external bit aiding, the role of ML based bit wipe-off
method and extended coherent integration time is
evaluated.
To begin, the navigation results from the vector-based
receiver are included in Figure 16 when bit decoding is
used to extend the coherent integration; this will later be
compared to the results using ultra-tight integration. Note
that the signal power based observation selection strategy
was not used in the vector-based receiver because the
reduction in the number of observations caused by the
strategy results in poor navigation performance. Results
show that the position solution with 100 ms of integration
no obvious improvement compared to the 20 ms (i.e., no
bit wipe-off needed) result in horizontal direction, and is
actually worse in vertical direction. This phenomenon is
due to errors in bit decoding and thus bit wipe-off, as
mentioned before.
It is noted that although methods of selecting suitable
correlator outputs for input into the ML bit decoding
algorithm (e.g., Ren et al, 2013) can mitigate the high
BER problem in the vector-based receiver, such
approaches were not used here. The reason is because the
results with external bit aiding shown in Table 2 represent
the best case scenario and these results are already far
worse than those of the ultra-tight receiver.
Figure 16 - RMS position and velocity errors in
different directions by using ML bit decoding based
bit wipe-off from vector-based receiver in the urban
canyon test
In the ultra-tight receiver, Figure 17 shows the
comparison of navigation results (position and velocity)
using coherent integration times of 20 ms, 100 ms using
external bit aiding (i.e., known bits) and 100 ms using bit
wipe-off from ML bit decoding. The position and velocity
accuracy has been markedly improved (more than 40%)
after extending coherent integration time from 20 ms to
100 ms in this case. Furthermore, as shown in Figure 17,
in the ultra-tight receiver, without any method of dealing
with the high BER problem, the navigation results with
100 ms from ML bit decoding are markedly better than
the 20 ms results, and very close to the 100 ms results
using external bit aiding. This implies that ultra-tight
receivers are more immune to the high BER problem, and
are thus better able to extend coherent integration time
compared to the vector-based receiver. In the ultra-tight
receiver with 100 ms from ML bit decoding, the RMS
position error in horizontal and vertical is less than 5 m
ITM 2014, Session B5, San Diego, CA, 27-29 January 2014 Page 12/14
and 10 m respectively, and the RMS velocity error in
horizontal and vertical is both less than 0.2 m/s.
Figure 17 - RMS position and velocity errors in
different directions by using ML bit decoding based
bit wipe-off from ultra-tight receiver in the urban
canyon test
Finally, the trajectory results from the standalone ultra-
tight GSNRx-hs-drTM
software receiver are shown in
Figure 18. With the coherent integration time of 100 ms
from ML bit decoding, the navigation solution is very
close to the reference trajectory in the urban canyon test.
Figure 18 – Trajectory results of standalone ultra-
tight receiver using the coherent integration time of
100 ms from ML bit decoding in the urban canyon
test. The blue line is the reference trajectory; the red
line is the result from standalone ultra-tight receiver.
CONCLUSIONS AND FUTURE WORK
This paper assesses the performance of ML bit decoding
in an ultra-tightly coupled GNSS receiver, presents an
analysis of the navigation performance with extended
coherent integration time after bit wipe-off, and gives an
update strategy for selecting the pseudorange and/or
Doppler measurements in a standalone software-based
ultra-tight GNSS receiver in weak signal environments.
The SDR of ML bit decoding in an ultra-tight receiver is
assessed as a function of received signal power. In the
context of GPS L1 C/A signals, the field test results show
that an ultra-tight receiver can improve the SDR by 1% –
30% over scalar-based receiver depending on the signal
strength. The ultra-tight receiver is also shown to be more
robust than the vector-based receiver, that it is more
immune to the high BER problem and does not require
any correlator outputs selection method in extending
coherent integration time. The navigation results show
that extended coherent integration with ML based bit
wipe-off method can help to improve the navigation
performance in signal challenged environments. The
position and velocity accuracy has been improved more
than 40% after extending coherent integration time from
20 ms to 100 ms in the vehicular navigation test. The
navigation results with extended coherent integration time
from ML bit decoding are close to those from external bit
aiding, and the latter are considered as containing no bit
errors and expected giving the best results.
A newly proposed signal power based observation
selection strategy has helped to overcome the inaccurate
GNSS measurements problem existing in heavy multipath
(fading) areas. It is shown that after implementing the
strategy in the standalone ultra-tight receiver with 100 ms
from ML bit decoding, the RMS position error in
horizontal and vertical is less than 5 m and 10 m
respectively, and the RMS velocity errors in horizontal
and vertical both are less than 0.2 m/s. This confirms the
feasibility of the standalone approach.
Future work will use more field tests under different
environments to test the robustness of this standalone
system.
ACKNOWLEDGMENTS
The research presented in this paper was conducted as
part of a collaborative research and development grant
between the University of Calgary, General Motors of
Canada, and Natural Sciences and Engineering Research
Council of Canada.
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