Transcript VRS Presentation

Investigations about the
VRS Methodology for
Network-RTK within
a Local Area
Presented by:
Ben Chan Siu-bun,
LS/G(HK&Is)
Acknowledgements
Abstracts
Contents
(Network-RTK)
• Carrier Phase Measurement and RTK Operation
• What is Network-RTK?
• How it works? What’s the theory and algorithm
of Network-RTK? How does it improve the
accuracy of position fixing?
• What are the core issues of VRS-RTK?
• Some common approaches – NetAdjust, FKP,
VRS
Contents
(The Project Investigation)
• Is VRS accurate? reliable? and robust?
• What constitutes a sound or weak VRS?
• Does VRS improve Static GPS? In terms of
accuracy and reliability.
• Does VRS improve RTK? In terms of
accuracy, reliability and robustness.
• What are the crucial factors affecting the
accuracy and performance of VRS? Could
the problem be resolved?
• Practical Issues for Implementation.
Carrier Phase Measurements.
Ambiguity
f s
s
  N   r  r
c
s
r
Count the
carrier cycle &
record the ɸ
0 + ϕ0
100 + ɸ1
200 + ϕ2
300 + ɸ3
:
s
r
ρ range between
satellite & receiver
ρ (s0 , r )
ρ (s1 , r) Approx r from
ρ (s2 , r ) pseudorange,
DGPS
ρ (s3 , r)
or others
:
Carrier Phase Measurement Errors
insignificant after
• satellite and receiver clock offsets double differencing
• satellite orbit error
Use predicted ephemeris of IGS,
may be significant even
• Multipath (station-dependent) for short baselines,
antenna-dependent
• antenna phase centre variation (station-dependent)
• ionospheric effect
• tropospheric effect
the most important distancedependent factors that affect
the accuracy of baselines over
long distances.
Double Differenced Carrier Phase Meas.
eo
ab
( a , b ) 
f eo e o
eo
 ab (T , T , Ta , Tb )  N ab
c

f
f
f
eo
eo
eo
eo
deph ab
 dion ab
 dtrop ab
 ab
c
c
c
• If the baseline length is short, say up to a few
km, errors in the satellite coordinates and
biases due to atmosphere can be assumed the
same; and the equation becomes
f eo e o
eo
eo
 ( a , b )   ab (T , T , Ta , Tb )  N ab
  ab
c
eo
ab
Double Differenced Carrier Phase Meas.
f eo e o
 ( a , b )   ab (T , T , Ta , Tb )  N abeo  abeo
c
eo
ab
• The above equation is effective for short baselines
since the effect of ionosphere, troposphere,
satellite orbit may in general be neglected.
• Any significant errors from these neglected terms
will spill over into the unknown parameters, namely
station coordinates and ambiguities; and
degrade the positional accuracy as well as the
integer nature of the ambiguity (HofmannWellenhof, 2001: p.213).
Static GPS using Carrier Phase
• Use precise orbit IONEX files downloaded
from the International GPS Service (IGS)
• Use model or Ionospheric-free frequency
to account for the dispersive effect of the
ionosphere (coed delay/ phase advance)
• Use a model e.g. Hopfield or Saastamoinen,
to account for the majority of the zenith
hydrostatic delay
• Solve for the troposheric delay bias (zenith
tropospheric scale factor = actual delay –
modelled delay) as an additional unknown
parameter in least squares
General RTK operation
• Transfer GPS signals of the Reference
Station (RS) to the rover
• Extrapolate the RS data to the same epochs
in which the corresponding rover
measurements have been generated
(software dependent)
• solve the carrier phase ambiguity in a short
time – with the help of either float
ambiguity or DGPS solution
• Compute the baseline (vector between RS
and rover)
Accuracy Performance of
RTK positioning
• Depends on the ability of the algorithms
to resolve the integer ambiguities and
to model the differential errors that
occur between the RS and the rover.
• To solve the ambiguity, many conditions must
exist, including a relatively short distance
between RS and rover.
Constraints of Single-base RTK
• Single Reference Station:- No independent check
• The station-dependent errors (multipath, APC
variation) of the RS would affect the RTK
accuracy
• Ionospheric, tropospheric and orbit errors at RS
and rover de-correlate as their inter-distance
increases  induce errors to integer ambiguity &
coordinates
Constraints of Single-base RTK
• What’s the effect? How serious?
 long Time to Fix Ambiguity (1 minutes? 1 hour?)
 unable to solve ambiguity
 incorrect ambiguity fix
 limited distance for RTK operation ( 10 km? 5 km ?
Or less?)
What is Network-RTK?
• The technique generally refers to those
investigations made on the optimal
means of processing data from Multiple
Reference Stations, and the Provision
of ‘Correction' information to users in
Real-time (Rizos, et al, 2002).
Basic Concepts of Network-RTK (1)
• Reference:- Rizos et al (2002), Lachapelle et al
(2000a) and Vollath et al (2002).
• Use a network of GPS reference stations
spreading over a wide geographic area to
reduce the distance-dependent errors in RTK
positioning.
• With different approaches of error modelling,
GPS observations at each single or pair of
reference stations are compared to the known
coordinates of the stations.
• Provision of a set of correction parameters at
the locations of the corresponding reference
stations over the area covered by the station
network.
Figure 1: Network Sketch
Figure 2: Rover transmits NMEA message
for VRS position to the network server
VRS
NMEA
Basic Concepts of Network-RTK (2)
• Such parameters are used to interpolate the
corrections to be made at any position of a rover.
• The interpolated correction could be applied
either direct in the RTK measurements or
through the generation of a virtual reference
station (VRS).  implies single or bi-directional
• The correction parameter (FKP) or VRS would
be broadcasted to the users through
telecommunication link. The use of either
method depends on the communication
infrastructure between the rovers and the GPS
reference network service provider. (Wübbena,
et al. , 2001b)
Figure 3: Network server transmits RTCM
correction stream for VRS position
VRS
RTCM
NMEA
Benefits of Network-RTK (1)
• The accuracy performance of RTK positioning
depends on the ability of the algorithms and
software to resolve the integer ambiguities and
to model the differential errors that occur
between the reference station(s) and the remote.
The use of a network of reference stations is
effective in enhancing the solution to both
problems (Lachapell et al, 2000b).
• help solve the integer ambiguities
 shorter search time ( initialization time )
 more reliable (independent check from RSs)
Benefits of Network-RTK (2)
• better model the differential errors between RS
and rover
 changes the error characteristics
The behavior of correlated errors (orbital and
atmospheric) can be modelled throughout the
region covered by the network (parameterization
techniques), while the uncorrelated errors (multipath
and noise) can be averaged out through filtering
 improve the performance of RTK positioning
• multiple RS  more availability of the service
• able to use RTK for long range positioning
Benefits of Network-RTK (3)
• Able to use spare network (less Reference
Stations) to achieve the same performance
• Wübbena et al (1996) showed that the postprocessing simulation of a reference station
network could reduce the effects to generally
less than 1 cm without any distance
dependencies.
Core Issues of Network-RTK
•
•
•
•
Modelling of Errors
Network Ambiguity Fixing
Error Interpolation
Broadcasting the correction
parameters
Core Issues of Network-RTK
(1) Modelling of Errors
Modelling of Errors (1)
• satellite orbit error
Use predicted ephemeris of IGS,
may be significant even
• Multipath (station-dependent) for short baselines,
antenna-dependent
• antenna phase centre variation (station-dependent)
insignificant after
• satellite and receiver clock offsets
double ifferencing
• ionospheric effect
• tropospheric effect
the most important distancedependent factors that affect
the accuracy of baselines over
long distances.
Modelling of Errors (2)
• GNSMART -- model all individual errors in the
State Space approach
• Trimble VRS -- models the ionospheric delays
by a simple 2-dimensional polynomials over
geomagnetic latitude and hour angle of the sun;
and handles the tropospheric errors with a
scaling technique based on the geometry of
receivers and satellites.
• Combinations of signal frequencies (e.g.
Ionospheric-free frequency) are commonly used
for the analysis of errors in GPS measurements
 eliminates the first order effect of the
ionosphere on the measurements.
Core Issues of Network-RTK
(2) Network Ambiguity Fixing
Network Ambiguity Fixing (1)
• After modelling of errors at each or pair of the
reference stations
• Solve the ambiguities of the 3 ‘baselines’ that
form the sides of a triangle of reference
station network by double differencing.
• Ambiguity resolution 'engine'
(Network Ambiguity Fixing)
–
–
–
–
–
handle double-differenced data from stations
operate in real-time
for each satellite
for every epoch
resolved with a fixed or float solution (software
dependent)
Network Ambiguity Fixing (2)
Prior to generation of the VRS data file
• The integer ambiguity for each of the 3
baselines fixed (for each epoch, each satellite)
• check on the triangle loop closure, say 20
mm (software dependent)
Core Issues of Network-RTK
(3) Interpolation and
Extrapolation
Interpolation and Extrapolation
• Calculate the remaining errors per station or
per ‘baseline’ of the triangle as compared to
the known coordinates of the 3 reference
stations.
• Interpolate/ Extrapolate the errors at the
approximate position of the rover.
• Generate the correction parameters for
Network-RTK or VRS.
Figure 4: Linear Interpolation Error
Figure 5: Interpolation and
Extrapolation
R2
R1
R3
R4
User 2
(extrapol.)
R5 User 1
R6
(interpol.)
Error Interpolation (1)
•
Usually the interpolation algorithms are
separately applied to the dispersive
(ionospheric) and to the non-dispersive
(geometric, i.e. tropospheric and orbit) biases.
•
The correction model parameters are known
as area correction parameters (in German
Flächenkorrekturparameter, abbreviated FKP).
Error Interpolation (2)
The common approaches of error interpolation
include
•
Linear Interpolation (Linear Combination
Model, Distance-based linear Interpolation
Method, Linear Interpolation Method)
•
Low-order Surface Model; and
•
Least-Squares Collocation.
(Dai, et al 2001).
– all the above methods significantly reduce
the distance-dependent biases in the
carrier phase and pseudo-range
measurements at the GPS user station;
– Similar performance of all the above
methods
Core Issues of Network-RTK
(4) Broadcast the
Correction Parameters
Broadcasting the correction
parameters (1)
Two steps:-
•
Prepare the correction parameters or
establishing a virtual reference stations with
corrected observations (rover position
requirement: method dependent)
•
Broadcast the correction data or
parameters to uses for RTK processing.
Broadcasting the correction
parameters (2)
(Wanninger, 2002)
Network observations on
common ambiguity level:
•
•
Broadcast of the observations of a master
reference station and observation
differences between pairs of reference
station, all being on the same ambiguity
level
With the network corrections and
information on their qualities, user performs
the interpolation step on his own
Broadcasting the correction
parameters (3)
Area Correction Parameter
•
•
(Flächenkorrekturparameter, FKP):
Broadcast of the observations of a master
RS and FKP (Wübbena et al. 2000)
The user applies the FKP to the reference
station observation data set according to
his position and thus obtains VRSobservations
Broadcasting the correction
parameters (4)
Gridded corrections
•
•
Broadcast of the observations of a master
reference station and gridded corrections of
the distance-dependent biases
The user interpolates individual corrections
within the grid and applies them to the
observation data set in order to obtain
VRS-observations
Broadcasting the correction
parameters (5)
Virtual Reference Station
•
•
The user sends his approximate position to
a central computing facility
by return receives VRS-observations to be
used for baseline positioning
Some Common Approaches
• NetAdjust Multi-Reference Station
Approach
• State Space Approach with FKP
• VRS Approach
NetAdjust Multi-Ref Station Approach (1)
• A condition adjustment method developed by
the University of Calgary (Raquet, 1998).
• Principle of the method
Use least squares to estimate the code and
carrier phase observable errors  improve
the integer ambiguity resolution over longer
distances (Lachapelle, et al. 2000b).
NetAdjust Multi-Ref Station Approach (2)
• The least squares condition is that all of
the double differences of the adjusted
measurements minus the calculated
ranges is zero (which would be true if
there were no errors).
• The calculated ranges ρ are the
distances between the known receiver
positions and the position of the
satellites as calculated from the
ephemeris data (Raquet, 1997).
Observation Equation
correction vector to carrierphase observables collected at
the user receiver, in metres,
Cross-covariance matrix
between the carrier-phase
observables collected at the
user receiver and at the
reference stations
double difference integer
ambiguity vector
between the reference
stations (assumed to be
known), in cycles
T
T
ˆ
lr  Clrl B ( BCl B )(B  N )
T
T
ˆ
l  Cl B (BCl B )(B  N )
correction vector to
carrier-phase
observables collected
at the reference
stations,in metres,
double difference
matrix (made up of
the values +1, -1 & 0)
covariance matrix of the carrierphase observables collected at
the reference stations
measurement-minus-range
(     )
carrier-phase observable
Cl
Representation of Error Modelling
• The covariance matrices Cl and Clrl
represent the behavior of the correlated
errors (ionospheric, tropospheric, and
satellite position errors) over the region
covered by the network and their dependency
on the satellite elevation. In addition, it is
necessary to know the variance of the
uncorrelated errors (multipath effects and
receiver noise) for each station in the
network ( Fortes, 2002).
Representation of Error Modelling
• The NetAdjust method models the correlated
errors and uncorrelated errors of the
reference and rover stations through the
covariance matrix of the carrier-phase
observables collected at the reference
stations, and the cross-covariance matrix
between the carrier-phase observables
collected at the user receiver and at the
reference stations.
Covariance matrix for modelling the
spatial correlation/ decorrelation
• Each of the correlated errors that affect GPS
positioning may not have the same behaviour
of spatial decorrelation across the region
covered by the reference network.
• The covariance matrix could cater for different
values in different regions and different time
of survey.
• Separate models for different kinds of
correlated errors among the multiple
reference stations, such as changing values
of ionospheric at different local time,
geographic location, season and solar cycle
(Fortes, 2002).
Figure: The NetAdjust Method
NetAdjust Multi-Ref Station Approach (2)
• The NetAdjust method corrects the reference
receiver measurements, as opposed to
providing differential range corrections to be
applied to the mobile receiver’s
measurements.
• Standard differential positioning or ambiguity
resolution is then performed between a
mobile receiver and one of the adjusted
reference receivers.
Accuracy of the Method
• The accuracy of the method depends on
 accurate information about the position of
the reference stations (!!)
 correct modelling of errors over the region
through the covariance matrix being tuned
by observations taken at the reference
stations at different location, different time,
different season and solar cycle.
Pre-assessment of the Performance
• The covariance function used by the
NetAdjust method can also be used to
determine the covariance matrix of the
corrected measurements.
• It provides a means to predict the
performance of the NetAdjust
corrections, without having the
measurements available (Raquet, 1998).
State Space Approach
State Space Approach
• Developed by Geo++® GmbH
• State Monitoring And Representation Technique
(SMART)  to analyze the data from a
reference station network to estimate and
represent the state of individual components of
the GPS error budget in real-time.
• Instead of generating just one lumped
parameters, the state of each error component
is determined from observations of a network of
reference stations. (Wübbena, et al. , 2001b)
GMSMART- Error Modelling
• Individual modelling of orbit, ionosphere, and
troposphere.
• Multipath effects – determined in the
simultaneous adjustment and complete
modeling of multi-station observations.
• Antenna phase center variations (APCV) –
corrected by using calibrated antennas. This
enables the use of different antenna types
within a network.
• Complete model for satellite receiver clocks.
• Aims to completely model the absolute
state of the system with carrier phase
accuracy (GNSS State Monitoring and
Representation Technique).
Modelling Undifferenced Data
• Undifferenced observation data is used in
modelling of errors.
• It was considered that the differencing
process eliminates not only the clock error
and time delays of the hardware, but also
operates on all other error sources. The
consequence is that all absolute error effects
are eliminated and only their differences
remain in the system. However, the
modelling of such differences becomes
markedly harder than the modelling of
undifferenced effects.
Parameters of GNSMART
• The determination of system state information
in a GNSS SMART system need not take place
in one continuous stream
• parameters of global character, e.g. satellite
orbits and clocks  drawn from global
networks
• regional parameters e.g. wide-area ionospheric
delay effects, local ionospheric disturbances
and tropospheric effects  drawn from
regional and local networks respectively.
Figure: Linear FKP planes for four
reference stations
(source: Wübbena, 2001a)
Trimble’s Virtual
Reference Station Method
VRS – General
• Developed by Trimble Terrasat
• 4 steps in the generation of VRS
 Error modelling
 Network Ambiguity Fixing
 Reference Data Dispplacement
 Error (Correction) Interpolation
Trimble’s VRS Approach (1)
• Data from the reference station network is
transferred to a computing center
• The network data is used to compute models
of ionospheric, tropospheric and orbit errors
• The carrier phase ambiguities are fixed for
the network baselines
• The actual errors on the baselines are
derived in cm accuracy using the fixed carrier
phase observations
Trimble’s VRS Approach (2)
• Linear or more sophisticated error models are
used to predict the errors at the user location
• A Virtual Reference Station (VRS) is created
at the user location
• The VRS data is transmitted to the user in
standard formats (RTCM).
(Vollath , et al, 2000,2002a)
VRS - Error modelling
Error modelling at 3 different levels
• Removal of coarse code outliers, coarse
carrier phase fluctuations and cycle slips by
comparing the pseudorange and carrier
phase measurements
• Use single difference of baseline
observations between two stations to remove
the common satellite clock error
• Use single layer ionospheric model and
tropospheric scaling technique to model the
atmospheric errors on undifferenced data
each station.
VRS - Network Ambiguity Fixing
• The ambiguity estimates NI from ionosphere
model and NC from troposphere model could
be mapped back into the original N1/N2
domain of the integer ambiguities of the basic
carrier phase observables on L1 and L2 with
the following equation:-
1

 Nc  
2
2



 N      2 1
2
 I  1

1

2

 N1 
2
2   N1 
2  1     T   
 2 
 N2 
N2 
2

1 
where N , N and 1 , 2 are the integer ambiguity and
wavelength of the L1 and L2 carrier phase
measurements respectively
1
2
VRS - Reference Data Displacement
• To make the transmitted data look like it came
from a different position, it has to be displaced
geometrically.
• The pseudorange between the satellite and the
virtual reference station can be approximated by
the appprox.pseudorange
between satellite and the
virtual reference station
the appprox. geometric range
between satellite and the
virtual reference station
~s
s
s
s
~
v  r  (Rv  Rr )
the pseudorange between
satellite and the original
reference station
the exact geometric range
between satellite and the
original reference station
VRS - Error Interpolation
• The differential errors between the 3 stations
of the triangle selected are used to set up a
linear model.
• One station of a triangle is selected as pivotal
station, with coordinates.
• The double differences between the stations
can be interpolated with the formula:
c (, )  PC, N  (  r )  PC,E  (  r ) cosr
Lat./ Long
Interpolation parameter
Figure 4: Linear Interpolation Error
VRS - Error Interpolation
• The differential errors between the 3 stations
of the triangle selected are used to set up a
linear model.
• Carrier phase measurements on ionosphere
on the ionospheric-free combination are
handled separately. One station of a triangle
is selected as pivotal station, with
coordinates ( ,  ) . The double differences
between the station can be interpolated with
the formula:
r
r
C (, )  PC, N    r   PC ,E    r  cosr
VRS – Error Interpolation
• Given the double differences to the other two
triangle stations ( ,  ) and ( ,  ) , the
interpolation parameters for the north
direction PC, N and PC ,E r for east are defined
by the equation:
1
1
2
2
C (1 , 1 )  PC , N  1  r   PC , E  1  r   cosr
C (2 , 1 )  PC , N  2  r   PC , E  2  r   cosr
• To interpolate or extrapolate, the formula
above is applied to the Virtual Reference
Station coordinates .
BREAK
The Project
Contents
(The Project Investigation)
• Is VRS accurate? reliable? and robust?
• What constitutes a sound or weak VRS?
• Does VRS improve Static GPS? In terms of
accuracy and reliability.
• Does VRS improve RTK? In terms of
accuracy, reliability, robustness and speed.
• What are the crucial factors affecting the
accuracy and performance of VRS? Could
the problem be resolved?
• Practical Issues for Implementation.
The Objectives
• Does VRS improve the performance of
GPS?
- in both Static GPS and RTK
• Does VRS provide a better solution than
Single Reference Station under all
circumstances?
What to investigate ?
– Compare the Performance
• What are the performances of the
single reference station, multiple
reference stations and VRS for
relative positioning, in terms of
accuracy, reliability, robustness and
speed? Does VRS provide a
solution better than the other two
methods?
What to investigate ?
– Temporal Changes
• Any temporal change to
performance of the single reference
station, multiple reference stations
and VRS methods, in terms of
accuracy, reliability, robustness and
speed?
What to investigate ?
– Factors affecting Performance
• What constitute a sound or weak
VRS? What are the crucial factors
affecting the VRS performance?
Design of the tests
• Positional accuracy
 achieved by the VRS
 achieved by single/ multiple reference
station methods
• Change of Positional Accuracy
 single reference station, multiple reference
stations and VRS
 at different time of observations
Figure: The Test Site
Fanling
Kam Tin
Lam Tei
Shatin
Siu Lang Shui
Stonecutter
0
5
10 km
Software
• Trimble Total Control - release 2.7.3 with the
modules of Network Adjustment and Postprocessing VRS
• The software could simulate real-time and
post-processing operations with functions that
control the use of observation data by
different time and processing mode settings.
The Test Control
The control
• 5 stations including Fanling, Kam Tin, Lam Tei
and Siu Lang Shui were surveyed on 8-15 Oct
2000. The other station at Stonecutter was
surveyed on 10-18 Oct 2002.
• The coordinates were determined by using
observations from two stations, Fanling and
Kau Yi Chau with respect to 6 IGS stations,
including Cocos Islands (Indian Ocean), Guam (Pacific Ocean),
Lhasa (Western China), Shanghai (Eastern China), Tsukuba
(Japan) and Yarragadee (Australia).
• 2 months of data, ITRF 96, GAMIT software
processing. The repeatability of the solution
(global accuracy) is 2-3cm.
Purposes of Re-computation of
Control Pt Coordinates
• To know if the published values are suitable
to be adopted as control in the test.
• To know the difference of the results of static
long observation (14 days) as compared to
the published values.
• So as to know if the difference between test
results (Static GPS and RTK) and the
published values are significant.
Table: Comparison between the
Published and Re-computed Coordinates
Station
Name
Recomputed Coordinates
(weighted mean of results
from 14-day observations)
Different from
Published
Values (mm)
Sigma (mm)
Latitude
Longitude
Height (m)
Lat
Long
Ht
Lat Long Ht
Fanling
N 22° 29'
40.87008''
E 114° 08'
17.40609''
41.2100
0
0
0
Kam Tin
N 22° 26'
41.66172''
E 114° 03'
59.63442''
34.5731
-6.0
4.5
9.1
0.7 0.3 2.0
Lam Tei
N 22° 25'
05.28288''
E 113° 59'
47.84457''
125.9356
4.8
7.5
0.6
1.3 1.6 3.2
Stonecut
ter
Siu Lang
Shui
N 22° 19'
19.81947''
E 114° 08'
28.27638''
20.2388
-0.9
-2.7 11.8 1.3 1.0 1.9
N 22° 22'
19.21710''
E 113° 55'
40.73309''
95.2910
5.7
9.3 -12.0 1.5 0.6 3.1
Shatin
N 22° 23'
42.97460''
E 114° 11'
03.27037''
258.7420
6.6
4.5 26.0 1.1 0.4 2.9
-
-
-
Static GPS
Positional Accuracy
by different
ref. station approaches
Does VRS improve Static GPS?
• Compare positional accuracy by
 single RS (Fanling 9.2 km)
 multiple RS (Fanling, Shatin, Lam Tei)
 VRS
• Use 1 hour data (static GPS)
• 24 sets of results (any temporal change)
Table: Results of Static GPS
1 hour observation data (1)
Single RS
Local Time
Multi RS
Lat
Long
Ht
Lat
Long
-14
-14
-23
-18
-12
-10
-9
-10
-13
11
-3
3
1
3
4
-39
9
-4
-7
-12
-11
-8
-5
-8
-9
-6
-6
5
9
14:00 – 15:00
-95
65
-129
15:00 – 16:00
-99
-35
16:00 – 17:00
17:00 – 18:00
-5
-4
18:00 – 19:00
19:00 – 20:00
08:00 – 09:00
09:00 – 10:00
10:00 – 11:00
11:00 – 12:00
12:00 – 13:00
13:00 – 14:00
VRS
Ht
Lat
Long
Ht
7
-4
6
-9
5
-9
11 -10
-27 -14
14 -10
-3
-4
-4
-5
-6
0
23
19
14
22
11
27
17 -19
13
-1
-8
5
-39
-7
-20
-9
-48
65
-8
-9
3
-6
2
0
17 -17
19
31
-2
-10
10
-9
-9
-1
12
-11
0
-10
-4
20
-11
-13
122
-13
-1
84
-8
-1
9
-103
5
Table: Results of Static GPS
1 hour observation data (2)
Single RS
Multi RS
VRS
Local Time
Lat
Long
Ht
Lat
Long
Ht
Lat
Long
Ht
20:00 – 21:00
83
216
-62
22
12
36
-
-
-
21:00 – 22:00
22:00 – 23:00
-8
-13
-2
-7
-1
1
-4
-5
-9
-7
10
-2
-3
-13
-1
-4
2
22
23:00 – 24:00
00:00 - 01:00
71
-8
66
0
117
-2
-6
12
-9
2
-3
-8
-8
-2
-7
-5
9
9
01:00 - 02:00
-5
-14
-4
9
-2
10
-7
-6
18
02:00 - 03:00
03:00 - 04:00
04:00 – 05:00
05:00 – 06:00
-31
-17
-9
-9
-1
-6
-7
-5
-11
-20
8
18
-3
-1
-1
-2
-4
-5
-3
-5
4
17
13
13
-6
-3
-4
0
-4
0
-1
2
21
34
20
14
06:00 – 07:00
-10
-11
30
-2
-11
20
-7
-3
24
07:00 – 07:59
-17
-10
22
-5
-8
11
-8
-4
20
Static GPS –
Performance and Quality of Single
Reference Station Method
• Single Reference Station
– Max. deviations
10 cm in latitude
22 cm in longitude
13 cm in height.
– 75% of the results deviate from the truth by
< 3 cm in horizontal,
<3 cm in height.
Static GPS –
Performance and Quality of Multiple
Reference Station Method
• Multiple Reference Station
– 100% of the lat/long results deviate from the
truth by < 2.5 cm
– 96% of the height displacements from the
truth by < 3.5 cm
Static GPS –
Performance and Quality of Multiple
Reference Station Method
• Virtual Reference Station
- Max. deviation from truth by
< 1.5 cm in lat./long
< 3.5 cm in height
- Positional accuracies
< 2 cm (1σ) in lat/ long
< 2.5 cm (1σ) in height.
Static GPS — Performance and
Quality of the VRS Method (1)
•
•
•
For static GPS using one-hour dual frequency
data, VRS could only be generated at certain
time (midnight and morning sessions) of a day
with the use of all satellite signals;
The failure of VRS generation was due to the
problem of network ambiguity fixing. Disabling
of 1 or more satellite signals would help fixing
the network ambiguity and improve the
generation of VRS;
With the suppression of selective satellite
signals, 21 out of 24 hourly sessions could
generate ‘an effective VRS’ that contains 5 or
more satellite data
Static GPS – Performance and
Quality of the VRS Method (2)
•
•
With ‘an effective VRS’
Note this
Max. deviation from truth
magnitude
< 1.5 cm in lat./long ;< 3.5 cm in height
Positional accuracies
< 2 cm (1σ) in lat/ long ; < 2.5 cm (1σ) in height.
A systematic bias : a few cm was found. All
results deviate from the truth in the same
southerly (lower latitude), westerly (lower value in
East longitude) and upper (higher elevation)
directions of the truth. This might be due to the
known coordinates of the reference stations or
other reasons. A good control of this bias is
necessary to ensure the quality of the VRS
solution.
A Sound VRS for Static GPS
• With signals of at least 5 satellites
generated.
• With sufficient VRS data (continuous)
• Positional accuracy of 2 cm (1σ) in lat/
long can be achieved in static GPS (1
hour observation)
What constitutes
a Weak VRS or even
Failure in VRS Generation ?
Investigate the Causes of
Failure in VRS generation
• Two tests were conducted on using data of
carefully planned time differences.
• Period of GPS satellites :11 hours and 58
minutes. Two series of tests were carried out
using data of 15 days (1 hour shift) and 157
days (10 hours 30 minutes shift) later
respectively.
• The results of static GPS using hourly data
with the method of VRS on 20 April 2003 and
9 September 2003.
Table: VRS Investigation Results (1)
5 April
Need to disable
SV?
Y/N
SV #
Sol’n
9 Sept
Need to
disable SV?
Y/N
SV #
Sol’n
08:00 – 09:00
N/A
VRS
09:00 – 10:00
N/A
VRS
08:00 – 09:00
N/A
-
10:00 – 11:00
N/A
VRS
09:00 – 10:00
N/A
-
11:00 – 12:00
N/A
VRS
10:00 – 11:00
N/A
-
VRS
12:00 – 13:00
N/A
VRS
11:00 – 12:00
N/A
-
VRS
13:00 – 14:00
Y
7
VRS
12:00 – 13:00
N/A
-
VRS
14:00 – 15:00
Y
10
VRS
13:00 – 14:00
N/A
-
VRS
15:00 – 16:00
Y
4,10
VRS
14:00 – 15:00
Y
26
VRS
16:00 – 17:00
Y
26
VRS
15:00 – 16:00
Y
-
-
17:00 – 18:00
N/A
-
VRS
16:00 – 17:00
Y
9,10,21
VRS
18:00 – 19:00
Y
9
VRS
17:00 – 18:00
Y
19:00 – 20:00
Y
17
VRS
18:00 – 19:00
Y
10,14,23
VRS
08:32 – 09:32
N/A
VRS
20:00 – 21:00
Y
-
-
19:00 – 20:00
N/A
-
VRS
09:32 – 10:32
N/A
VRS
21:00 – 22:00
Y
5
VRS
20:00 – 21:00
N/A
-
VRS
10:32 – 11:32
N/A
VRS
22:00 – 23:00
Y
25
VRS
21:00 – 22:00
N/A
-
VRS
11:32 – 12:32
N/A
VRS
23:00 – 24:00
Y
25
VRS
22:00 – 23:00
N/A
VRS
12:32 – 13:32
N/A
VRS
00:00 - 01:00
N/A
VRS
23:00 – 24:00
N/A
VRS
13:32 – 14:32
Y
20 April
Need to
disable SV?
Y/N
SV #
Sol’n
-
2,16
VRS
Remarks:- Periods of no VRS or VRS with less than 5 satellites are highlighted
Table: VRS Investigation Results (2)
5 April
Need to disable
SV?
Y/N
SV #
Sol’n
Need to
disable SV?
Y/N
SV #
Sol’n
Need to
disable SV?
Y/N
SV #
20 April
9 Sept
Sol’n
00:00 - 01:00
N/A
VRS
23:00 – 24:00
N/A
VRS
13:32 – 14:32
Y
2,16
VRS
01:00 - 02:00
N/A
VRS
00:00 - 01:00
N/A
VRS
14:32 – 15:32
Y
-
-
02:00 - 03:00
N/A
VRS
01:00 - 02:00
N/A
VRS
15:32 – 16:32
Y
1,3,6,14
VRS
03:00 - 04:00
N/A
VRS
02:00 - 03:00
N/A
VRS
16:32 – 17:32
Y
13,14,31
VRS
04:00 – 05:00
N/A
VRS
03:00 - 04:00
N/A
VRS
17:32 – 18:32
Y
-
-
05:00 – 06:00
N/A
VRS
04:00 – 05:00
N/A
VRS
18:32 – 19:32
Y
11,15,27
VRS
06:00 – 07:00
N/A
VRS
05:00 – 06:00
N/A
VRS
19:32 – 20:32
Y
13,15,20,25
31,VRS
07:00 – 07:59
N/A
VRS
06:00 – 07:00
N/A
VRS
20:32 – 21:32
N/A
VRS
08:00 – 09:00
VRS
06:56 – 07:56
N/A
VRS
21:28 – 22:28
N/A
VRS
09:00 – 10:00
VRS
08:00 – 09:00
N/A
-
VRS
22:28 – 23:28
N/A
VRS
10:00 – 11:00
VRS
09:00 – 10:00
N/A
-
VRS
23:28 – 00:28
N/A
VRS
11:00 – 12:00
VRS
10:00 – 11:00
N/A
-
VRS
00:28 – 01:28
N/A
VRS
12:00 – 13:00
VRS
11:00 – 12:00
N/A
-
VRS
01:28 – 02:28
N/A
VRS
13:00 – 14:00
VRS
12:00 – 13:00
N/A
-
VRS
02:28 – 03:28
N/A
VRS
14:00 – 15:00
VRS
13:00 – 14:00
N/A
-
VRS
03:28 – 04:28
N/A
VRS
15:00 – 16:00
Y
4,10
VRS
14:00 – 15:00
Y
26
VRS
04:28 – 05:28
N/A
VRS
16:00 – 17:00
Y
26
VRS
15:00 – 16:00
Y
-
-
05:28 – 06:28
N/A
VRS
VRS
16:00 – 17:00
Y
9,10,21
VRS
06:28 – 07:28
N/A
VRS
17:00 – 18:00
Remarks:- Periods of no VRS or VRS with less than 5 satellites are highlighted
The Causes of Failure in VRS generation
• The weak VRS solution (not being able to
generate VRS with 5 satellite signals)  not
due to the satellite configuration.
• Weak VRS all happened in the afternoon
and/or early evening.
• Inspect the VRS observation data file
No. of satellite plots of the VRS file
Satellite elevation plot of the VRS file
The Causes of Failure in VRS generation
• The weak VRS solution (not being able to
generate VRS with 5 satellite signals) 
not due to the satellite configuration.
• Weak VRS all happened in the afternoon
and/or early evening.
• Inspect the VRS observation data file
• Inspect the Network Ambiguity Fixing
What constitutes a Sound VRS?
• The stringent requirements of network
ambiguity fixing  provide an effective means
for the quality control of the satellite signals to
be used for the generation of VRS
 make the VRS solution more reliable.
• Satellite signals that could not resolve the
baseline ambiguity might to certain extent be
contaminated by multipath effects,
atmospheric disturbances or other effects.
Such satellite signals, failed in network
ambiguity fixing, would not be used in the
generation of the VRS.
Baseline Ambiguity Resolution
(with all satellite signals)
Ambiguity Resolution of
Different Baselines
VRS generated after
selective disabling SV ?
Sessions of
observatio
n
(local time)
Shatin/
Lam Tei
Fanling/
Lam Tei
Fanling/
Shatin
Yes/No
SV disabled
13:00 – 14:00
Float LIF
Fixed LIF
Fixed LIF
Y
7
14:00 – 15:00
Fixed LIF
Float LIF
Float LIF
Y
10
15:00 – 16:00
Float LIF
Float LIF
Float LIF
Y*
4,10
16:00 – 17:00
Fixed LIF
Fixed LIF
Float LIF
Y*
26
18:00 – 19:00
Fixed LIF
Fixed LIF
Float LIF
Y
9
19:00 – 20:00
Float LIF
Fixed LIF
Float LIF
Y
17
20:00 – 21:00
Fixed LIF
Float LIF
Float LIF
N
-
21:00 – 22:00
Float LIF
Fixed LIF
Fixed LIF
Y
5
22:00 – 23:00
Fixed LIF
Fixed LIF
Float LIF
Y
25
23:00 – 24:00
Fixed LIF
Float LIF
Fixed LIF
Y
25
Critical examination of the
observation data for the
cause of failure in
Network Ambiguity Fixing
• As the Fanling/Shatin baseline has the most
serious problem in ambiguity fixing, the
corresponding 7 sessions of observations
with unfixed ambiguity were selected for
examination and analysis of the causes of
failure in ambiguity fixing.
Critical examination of the
observation data
•
•
•
Double difference L1 carrier phase residual
 detects cycle slips, noise remaining after
double difference
 L2 is often with more noise than L1.
Double difference range residual
 cycle slips, multipath, ionospheric effect
and other measurement noises that have
different amount of influence on code and
carrier phase measurements.
Double difference ionospheric residual
show the magnitude of ionospheric
residuals of a baseline observation not being
cancelled out double difference.
Critical examination of the
observation data
Double Difference
Phase Residuals –
On 5 April 2003
At local time
19:00 – 20:00
(Ref: SV09)
Double Difference
Range Residuals –
On 5 April 2003
At local time
19:00 – 20:00
(Ref: SV09)
Double Difference
Ionospheric Residuals
On 5 April 2003
At local time
19:00 – 20:00
(Ref: SV09)
Analysis by Stacking Results on
Consecutive Days of Observations
•
The stacking results of static positioning at
consecutive days of observations would
show the same pattern if multipath exists
under certain satellite-receiver geometry
and the signals pass through the same
atmospheric conditions.
Stacking Results on Consecutive
Days of Observations
Factors affecting the VRS Performance (1)
• The network ambiguity fixing is found to be
the first and most stringent requirement to
meet in generating the VRS.
• Fixing the network ambiguity is a real
challenge even for short base of 10 km in the
Hong Kong environment.
• Elevation angles of satellite signals, multipath
effects, decorrelated ionospheric effects and
decorrelated tropospheric delay bias at the
reference stations are found to be the most
important factors.
Factors affecting the VRS Performance (2)
• In general, signals at low elevations up to 25o
would be more seriously affected by tropospheric,
ionospheric and multipath effects.
• The ionospheric effects on satellite signals were
not prominent after double differencing (in this
test project). Most of the double difference
ionospheric residuals are less than 0.5 cycle.
• Ionosphere scintillation effects in the nearequatorial regions are at approximately 1 hour
after local sunset to local midnight might account
for the problems of network ambiguity fixing in the
evening sessions until midnight.
Findings - for generating an
effective VRS
• network ambiguity fixing is an important
process in the generation of VRS
• the VRS observation data file must be with
sufficient data
 sufficient number of satellites at the same
epoch. 4 satellites are required for static
GPS whilst 5 satellites are needed in RTK
processing
 continuity of data for a certain period of
time. (Observation data in a VRS file
discontinuous with gaps  not being able to
serve as a functional VRS for static or
kinematic GPS surveys.)
Factors affecting the Network
Ambiguity Fixing
•
•
•
•
•
•
•
•
Satellite geometry
Elevation angles of satellite signals
Multipath effects
Cycle slips and other signal noises
Decorrelated ionospheric effects at the reference
stations
Decorrelated tropospheric delay bias at the
reference stations
Decorrelated effects of the orbit errors at the
reference stations
Antenna phase centre variation is not included for the
same type of choke ring antenna was used in all
reference stations of this project.
Factors affecting the VRS
performance
• Network ambiguity fixing
• Accurate known coordinates of the reference stations
• Station-dependent errors such as multipath (if not
rejected in network ambiguity resolution and the
VRS generation process) would be absorbed in the
interpolation errors and become errors on the
correction parameters.
• Meteorological information at the unknown station
would significantly affect the resulting coordinates
of the unknown station in the VRS methods with the
use of tropospheric model.
Effect of Meteo Data
Deviated from Published Values (mm)
Same
Temperature
& humidity
Lat
Long
Ht
-9
-4
14
Lat
Temperature Change
Long
Ht
Humidity Change
+2.5 °C
-16
-2
-37
+5%
-7
-5
39
+5 °C
4
-5
118
+10%
-2
-4
57
+10 °C
9
14
251
+20%
4
-5
98
-2.5 °C
-1
-5
64
-5%
-12
-3
-11
-5 °C
-19
19
-96
-10%
-16
-2
-36
-10 °C
-25
41
-181
-20%
-19
20
-101
Temperature and Humidity Change
+5 °C
13
6
150
11
+20%
5
1
166
10
226
+5 °C +5%
+5 °C +10%
Effect of temperature and humidity change (at the unknown station) on the positional
accuracy
Effect of Inaccurate Meteo Data
• Inaccurate meteorological information
 be absorbed in the error modelling at the
reference stations and affect the resulting
coordinates of the unknown station through
the process of error interpolation
 lead to failure in network ambiguity fixing
and problem in the generation of VRS under
the Trimble’s algorithm
Does VRS improve RTK ?
• Compare RTK positional accuracy by
 single RS (Fanling 9.2 km)
 VRS
• Compare TTFA
 single RS (Fanling 9.2 km)
 VRS
• Assess the time required for VRS
generation for RTK (with 5-minute
backward data)
Two series of tests
Single-base RTK
• at every 5 minutes, local time 8:00 - 10:00
(continuous performance)
• at every 30 minutes for the whole day
(change of accuracy at different time and
conditions of a day)
VRS-RTK
• at every 5 minutes, local time 08:00 - 14:00
(continuous performance and the behaviour
of VRS generation)
• at every 30 minutes for the remaining hours
of the day
Single-base RTK Positioning (2 hrs)
Latitude Shift - Single-base RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
100
80
60
40
20
0
-20
-40
-60
-80
-100
+ Sigma
6
4
PDOP
8
Latitude
- Sigma
PDOP
2
09:55
09:45
09:35
09:25
09:15
09:05
08:55
08:45
08:35
08:25
08:15
08:05
0
Local Time 5 April 2003 (2 hrs)
Longitude Shift - Single-base RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
20
15
10
5
0
-5
-10
-15
-20
-25
-30
+ Sigma
6
4
PDOP
8
Longitude
- Sigma
PDOP
2
09:55
09:45
09:35
09:25
09:15
09:05
08:55
08:45
08:35
08:25
08:15
08:05
0
Local Time 5 April 2003 (2 hrs)
Height Displacements - Single-base RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
150
130
110
90
70
50
30
10
-10
-30
-50
-70
-90
-110
-130
-150
+ Sigma
6
4
2
Local Time 5 April 2003 (2 hrs)
09:55
09:45
09:35
09:25
09:15
09:05
08:55
08:45
08:35
08:25
08:15
08:05
0
PDOP
8
Height
- Sigma
PDOP
VRS-RTK Positioning (4.5 hrs)
Latitude Shift - VRS-RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
-40
+ Sigma
6
4
PDOP
8
Latitude
- Sigma
PDOP
2
Local Time 5 April 2003
12:25
12:05
11:45
11:25
11:05
10:45
10:25
10:05
09:45
09:25
09:05
08:45
08:25
08:05
0
(4.5 hrs)
Longitude Shift - VRS-RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
20
15
10
5
0
-5
-10
-15
-20
+ Sigma
6
4
PDOP
8
Longitude
- Sigma
PDOP
2
12:25
12:05
11:45
11:25
11:05
10:45
10:25
10:05
09:45
09:25
09:05
08:45
08:25
08:05
0
Local Time 5 April 2003 (4.5 hrs)
Height Displacements - VRS-RTK
(favourable atmospheric conditions)
10
Diff from Published
Value (mm)
70
60
50
40
30
20
10
0
-10
-20
-30
-40
+ Sigma
6
4
2
Local Time 5 April 2003 (4.5 hrs)
12:25
12:05
11:45
11:25
11:05
10:45
10:25
10:05
09:45
09:25
09:05
08:45
08:25
08:05
0
PDOP
8
Height
- Sigma
PDOP
Findings: single-base RTK vs VRS-RTK
under favourable atmospheric conditions
• Single-base-RTK
< 7cm Lat , 2 cm Long
< 10 cm in height
• VRS-RTK:
< 2 cm Lat & Long.
< 4 cm height from the truth.
• The VRS-RTK is more reliable and consistent in
achieving high positional accuracy under the
favourable atmospheric conditions, even during
the periods with time slots of PDOP=8 (due to
correction already built in the VRS)
• a systematic shift was found in the test results of
the VRS-RTK method. This bias must be
controlled to ensure high accuracy results.
Results of TTFA
single-base RTK and VRS-RTK
TTFA (s)
SingleVRSbase
RTK
RTK
8
7
Local
Time
PDO
P
No
of
SV
08:05
6.3
5
08:10
7.6
5
11
08:15
8.6
5
08:20
8.4
08:25
TTFA (s)
SingleVRSbase
RTK
RTK
4
4
Local
Time
PDO
P
No
of
SV
09:05
2.1
7
5
09:10
2.1
7
4
4
8
9
09:15
2.7
6
4
4
5
8
10
09:20
2.9
6
4
5
2
6
6
14
09:25
3.2
6
4
4
08:30
2.1
6
7
4
09:30
3.4
6
4
4
08:35
2.1
6
4
4
09:35
2.7
7
4
4
08:40
1.8
7
6
4
09:40
2.9
7
5
5
08:45
1.8
7
4
4
09:45
3.1
7
4
4
08:50
1.9
7
4
4
09:50
3.2
7
4
4
08:55
1.9
7
4
4
09:55
3.2
7
4
4
09:00
2
7
4
4
10:00
3.2
7
4
4
Findings: TTFA by using
single-base RTK and VRS-RTK
• The time to fix ambiguity (TTFA) in both
single-base-RTK and VRS-RTK methods
under favourable atmospheric conditions are
more or less the same.
• TTFA within 4 seconds in either method.
• Both methods indicate that a longer TTFA
would be required as PDOP > 4 or number of
satellites < 6. However, this relationship does
not hold under the unfavourable atmospheric
conditions of observations in the afternoon
and evening.
Single-base RTK Positioning (24 hrs)
Latitude Shift - Single-base-RTK
25
100
20
50
0
15
-50
10
+ Sigma
PDOP
Diff from Published
Value (mm)
150
Latitude
- Sigma
PDOP
-100
5
-150
07:00
04:30
02:00
23:30
21:00
18:30
16:00
13:30
11:00
09:45
09:20
08:55
08:30
0
08:05
-200
Local Time 5 - 6 April 2003 (24 hrs)
Longitude Shift - Single-base-RTK
25
Diff from Published Value
(mm)
200
150
100
50
0
-50
-100
-150
-200
-250
20
10
+ Sigma
PDOP
15
Longitude
- Sigma
PDOP
5
07:00
04:30
02:00
23:30
21:00
18:30
16:00
13:30
11:00
09:45
09:20
08:55
08:30
08:05
0
Local Time 5 - 6 April 2003 (24 hrs)
Height Displacements - Single-base-RTK
25
Diff from Published
Value (mm)
400
350
300
250
200
150
100
50
0
-50
-100
-150
-200
-250
-300
-350
-400
-450
20
10
+ Sigma
PDOP
15
Height
- Sigma
PDOP
5
Local Time 5 - 6 April 2003 (24 hrs)
07:00
04:30
02:00
23:30
21:00
18:30
16:00
13:30
11:00
09:45
09:20
08:55
08:30
08:05
0
VRS-RTK Positioning (24 hrs)
Latitude Shift - VRS-RTK
10
Diff from Published
Value (mm)
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-35
-40
-45
-50
8
4
+ Sigma
PDOP
6
Latitude
- Sigma
PDOP
2
07:00
03:30
00:00
20:30
17:00
13:55
13:20
12:45
12:10
11:35
11:00
10:25
09:50
09:15
08:40
08:05
0
Local Time 5 - 6 April 2003 (24 hrs)
Longitude Shift - VRS-RTK
10
8
+ Sigma
6
Longitude
4
PDOP
Diff from Published
Value (mm)
20
15
10
5
0
-5
-10
-15
-20
-25
-30
- Sigma
PDOP
2
07:00
03:30
00:00
20:30
17:00
13:55
13:20
12:45
12:10
11:35
11:00
10:25
09:50
09:15
08:40
08:05
0
Local Time 5 - 6 April 2003 (24 hrs)
Height Displacements - VRS-RTK
10
Diff from Published
Value (mm)
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
8
4
+ Sigma
PDOP
6
Height
- Sigma
PDOP
2
Local Time 5 - 6 April 2003 (24 hrs)
07:00
03:30
00:00
20:30
17:00
13:55
13:20
12:45
12:10
11:35
11:00
10:25
09:50
09:15
08:40
08:05
0
Table: Compare the Overall Performance of
Single-base RTK & VRS-RTK
Latitude/ longitude shift
(shift +/-2 sigma)
0 – 30 mm
No. of cases (tests)
SingleVRS-RTK
base RTK
6
5
31 – 60 mm
19
12
61 – 100 mm
6
6
101 – 200 mm
4
0
200 – 500 mm
5
0
> 500 mm
5
0
No RTK solutions
3
25
Findings: Overall Performances of Singlebase RTK & VRS-RTK
• Both methods have 5 to 6 results (1.2%) that
can achieve a high accuracy of less than 3 cm
deviation from the truth at 95% level of
confidence (2 sigma).
• All VRS-RTK solutions < 10 cm (2 sigma) shift
from the truth.
• 55% (25/45) of the single-base-RTK solutions
shift from the truth < 10 cm (2 sigma).
• Single-base-RTK continues to provide
solutions under unfavourable conditions (need
1-2 minutes in ambiguity fixing)
• VRS-RTK stopped to provide the low accuracy
solution in unfavourable conditions.
Table: Successful VRS generation
using hourly data of all satellite data
Local Time
(Hourly
sessions)
Any VRS (with 5 or more
satellites) be generated by using
all satellite data?
08:00 – 13:00
Yes
13:00 – 17:00
No
17:00 – 18:00
Yes
18:00 – 00:00
No
00:00 - 08:00
Yes
Table: Time required for VRS
generation using raw data
Local Time
08:00- 12:10
12:15 – 12:30
Time for
VRS
generation
5s
5 - 18 s
Any VRS file with 5 or
more satellites
generated? (Yes/ No)
Yes
Yes
12:35 – 00:00
>5s
(up to > 1 hr)
No
00:30 – 07:30
5s
Yes
Table: Start an End Time of VRS
generation under adverse conditions
Local
Time
12:15
Time to
generate
VRS (mins)
8
VRS Generation
Start Time
End Time
12:07
12:15
No. of
satellites
in the
VRS file
5
Able to
provide
RTK
solution?
Yes
12:20
13
12:07
12:20
5
Yes
12:25
18
12:07
12:25
5
Yes
12:45
16
12:29
12:45
3
No
12:50
21
12:29
12:50
3
No
12:55
26
12:29
12:55
3
No
13:00
31
12:29
13:00
3
No
13:10
26
12:44
13:10
4
No
13:15
31
12:44
13:15
4
No
13:20
36
12:44
13:20
4
No
13:25
42
12:43
13:25
3
No
13:30
47
12:43
13:30
3
No
13:35
52
12:43
13:35
3
No
13:40
57
12:43
13:40
3
No
13:45
62
12:43
13:45
3
No
13:50
67
12:43
13:50
2
No
Table: Time to Generate VRS-RTK
Local
Time
Time
to
Generate
VRS
(mins)
Diff from Published
Values (mm)
PDOP
No
Of
SV
Sigma (mm)
TTFA
(s)
Lat
Long
Ht
Lat Long
Ht
08:01 1 second 5.2
5
13
3
-3
31 17
5
30
09:00
10:00
11:00
12:00
7
7
5
5
4
4
7
5
-3
-9
-10
-9
-5
-4
-4
-5
32
11
10
23
3
5
1
9
8
18
2
25
1 second
1 second
1 second
1 second
2.0
3.2
3.0
3.1
4
5
1
8
Time required for VRS generation
• The test results show that the increase of the
observation time does not help improve the
VRS generation.
• The successful generation of VRS depends
on the network ambiguity fixing that can only
be possible with satellite signals not
adversely affected by multipath, atmospheric
bias and other noises remained after double
differencing.
• Even 1 second of data could generate a
correct VRS under favourable atmospheric
conditions.
Practical Issues
for Implementation
of Network-RTK
Practical Issues for Implementation
•
•
•
•
RTCM format for data transmission
Choice of Ephemeris data
Effect of Meteorological Data
Understand different effects on GPS
measurements
•
:
•
:
GPS Data Transmission
• RTCM (Radio Technical Commission for
Maritime) Services Special Committee 104 v
2.2
• Initially format for DGPS messages (v 2.1)
• DGPS corrections include:- ephemeris errors,
SV clock prediction errors, ionospheric biases,
tropospheric biases, differential tropospheric
delay errors
Fixed RTCM Messages (1 … 12)
1
2
3
5
6
7
9
10
11
12
Differential GPS corrections
Delta Diff GPS Corrections
GPS reference station parameters
GPS constellation health
GPS null frame
DGPS beacon almanac
GPS partial correction set
P-code differential corrections
C/A code Ld Delta corrections
Pseudolite station parameter
Fixed RTCM Messages (15 … 59)
15 (T)
16
17(T)
18
19
31-37
59
Ionospheric Delay Message
GPS Special Message
GPS Ephemerides
RTK uncorrelated carrier phases
RTK uncorrelated pseudoranges
GLONASS Diff Message
Propriety Message
Choice of Ephemeris data
Broadcast
2.6 m
Predicted
Ultra-Rapid
(12 hrs ahead)
0.25 m
Final
The importance of Meteo
Information at RS and Rover
• Different meteorological information would
lead to different amount of corrections made
by the tropospheric model to the GPS
measurements.
• As advised in (Hugentobler, et al., 2001: p.
195), the surface meteorological information
and tropospheric parameters are important
except for those areas with network < 10km
diameter and height differences <100
metres.
Scintillation effects
• Klobuchar (1996) – “the time of strong
scintillation effects in the near-equatorial
regions are at approximately 1 hour after
local sunset to local midnight.” Hong Kong is
inside the near-equatorial region of maximum
scintillation.
• “Precise GPS measurements should be
avoided during the approximate local time
19:00- 24:00 (in the near-equatorial region)
during the year of high solar activity, and
during the months of normally high
scintillation activity (April to August for Hong
Kong) to reduce the chance of encountering
scintillation effects.
The Atmosphere (1)
20,200 km
GPS Satellites
350 km
IONOSPHERE
120 km
85 km
50 km
14 km
0 km
Thermosphere
Mesosphere
Stratosphere
TROPOSPHERE (water vapour & gas)
The Atmosphere (2)
20,200 km
350 km
14 km
GPS Satellites
IONOSPHERE
Thermosphere
Mesosphere
Stratosphere
TROPOSPHERE (water vapour & gas)
Quoted Examples on Network-RTK
Current GNSMART Networks
Production:
• Dubai – 5 stations
• WALCORS, Belgium – 23 stations
• COSMOS, Russia – 7+19 stations
• OSI, Ireland – 16 stations
• LGTB, Switzerland – 6 stations
Evaluation:
• Ordnance Survey GB – 23 stations
• Italy - Milan; Turin
• Hong Kong
Our Challenges
Key Skills ( extracted from GNSS
Applications & Market, IESSG lecture notes)
• Knowledge of GPS and Galileo
• Knowledge of Regional and Local Augmentation
System
• Awareness of other ‘position’ sensors, e.g. INS
• Ability to integrate Satellite Navigation with other
sensors
• Awareness of communication options thru’
terrestrial, satellite, local and global aspects
• Ability to develop Combined
positioning/communication systems
• Knowledge of Key Markets and Applications
Open Discussions