Bulletin Grodrsique (1992) 66: 2 - I l Bulletin G6od6sique

© Springer-Verlag 1992

A consistency test of airborne GPS using multiple monitor stations M.E. Cannon l, K.P. Schwarz 1, M. Wei 1, and D. Delikaraoglou 2

1 Department of Surveying Engineering, The University of Calgary, Calgary, Alberta, Canada z Canada Centre for Surveying, Energy, Mines and Resources, Ottawa, Ontario, Canada

Received May 8; Accepted August 1, 1991

ABSTRACT

In October 1990, several airborne GPS tests were conducted in the Ottawa region by the Canada Centre for Surveying (CCS) and the Canada Centre for Remote Sensing (CCRS). Ashtech XII receivers were located at up to three monitor stations with baseline lengths to the aircraft ranging from 1-200 km. Approximately two hours of airborne data, collected at a 2 Hz rate, were available for each of the three test days. Post-processing of the differential data was done using the University of Calgary's SEMIKIN package which utilizes a Kalman filter algorithm to estimate both the remote receiver's position and velocity. Comparisons were made between the aircraft position and velocity determined from each of the monitor stations to assess the consistency of differential GPS when different reference stations are used. Results show that the degree of consistency is dependent upon the distance to the monitor stations. Agreement at the decimetre-level is achieved in position when the baseline lengths are within 100 km. Agreement in velocity is usually better than 1 cm s -I (RMS).

In troduct ion

Kinematic GPS in airborne mode has become an active area of research during the last five years, see for instance Mader (1986), Landau (1989), Schwarz et al (1989), and Seeber and Wtibbena (1989). Among the numerous applications those in photogrammetry and in airborne gravity have received special attention, see for instance Lucas and Mader (1989), Cannon and Schwarz (1990), Kleusberg et al (1990), Hehl et al (1990), Knickmeyer (1990), Schwarz

Offprint requests to: M, E. Cannon

et al (1991a). When not affected by cycle slips or by problems in the initial ambiguity resolution, results have reached an accuracy that is difficult to match by any other system. Inverse photogrammetry, using dense and accurate ground control, seems to be the only procedure that gives results of comparable accuracy. Although its use in testing kinematic GPS is very appropriate, because it supplies completely independent control, its use in survey operations is both awkward and expensive.

When looking for a system that would ensure increased reliability in operations, a configuration with multiple GPS monitor stations comes immediately to mind. Each monitor station gives an independently estimated trajectory for the airborne antenna, i.e. a time series with the same independent parameter. Comparison of the different estimates is therefore simple as is the computation of a weighted mean. Beside simplicity in operations, the system has the advantage of being inexpensive in terms of hardware and self-monitoring with respect to the ground stations.

A configuration of this kind does not provide completely independent control. Errors in the airbome system will be common to all estimated trajectories. Similarly, the different ground receivers may show a similar error pattern when deployed in a relatively small geographic area. However, the configuration provides a good consistency check and improved reliability in cycle slip correction.

The test flights conducted in October 1990 in the Ottawa area provided an excellent test bed for studying the consistency of trajectory determination by airbome GPS. They also offered an independent check on some of the results by

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the information coming from an synchronized INS. The paper gives a first analysis of the data and draws some tentative conclusions.

Description the Airborne Tests

GPS/INS tests were carded out on October 3 (Day 276), 5 (Day 278) and 16 (Day 289), 1990. Ashtech XII receivers were used for the GPS segment of the data collection. Monitor receivers were placed at up to three stations and simultaneous data were collected at each site to all satellites in view. As well, an Ashtech receiver was installed in a Falcon 20 aircraft with the antenna mounted on the fuselage. Each GPS receiver internally recorded raw data at a 2 Hz rate. The ground receivers collected L1 and L2 (squaring) data, while the aircraft receiver only recorded L1 data. A Litton 51 stable platform INS, which was also installed in the aircraft, collected velocity and position data at a 20 Hz rate and attitude at a 100 Hz rate. The time tagging between the two data streams was precise to one millisecond and the offset between the airborne GPS antenna phase centre and the INS centre was determined in the body frame system of the aircraft. Due to limitations in the INS data recording device, only data along straight flight lines were stored, thus the measurements are discontinuous between flight lines.

The three GPS monitor locations are Metcalfe, Mallorytown and Telescope. Figure 1 illustrates the layout of the monitor and airport stations that were occupied during the tests. The coordinates of the points are accurate to a few ppm. Shown in Figure 2 is a typical aircraft flight path (from Day 278) in which the aircraft circled above the Metcalfe and Mallorytown stations but only came

" " " 2 1 8 k m - _..

239 km

Metcalfe

! 92 km

Mallorytown

Figure 1: Location of Monitor and Airport Control Points

46.5

46.0 ~ , Telescope

"~ 45.5

. M

~ 4 4 . 5

44.0

43.5 !

-78.0 -77.5

Figure 2:

\ :..

" . . . ~:IVIetcalfe

MaUorytown

! |

-77.0 -76.5 -7'6.0 -7'5.5 -75.0 Longitude (deg)

Aircraft Flight Path on Day 278

within 85 km of the Telescope station. Baseline separations between the monitor stations as well as between the monitor stations and the aircraft (remote) are given in Table 1.

Table 2 summarizes the GPS observation schedule for the three test days. On Day 276, monitor receivers were set-up at Metcalfe and Mallorytown, however, power problems occurred at Metcalfe, rendering this data useless since no kinematic data were recovered. On Day 278, no problems were encountered at any of the three monitor receivers. Power problems also occurred at Metcalfe on Day 289, however, about one third of the raw data were recovered. No data recording problems occurred with the aircraft receiver during any of the three test days.

The observation span of about 2-3 hours per day included a short static survey (about 5-10 minutes) at the beginning and end of the run. Up to seven satellites were tracked at any one time,

Table 1: Baseline Separations Between Monitor Receivers and Monitor-

Remote Receivers

Basel ine

Metcaife-Mallorytown Metcalfe-Telescope Mallorytown-Telescope Metcalfe-Aircraft: initial

during mission Mallorytown-Aircraft: initial

during mission Telescope-Aircraft: initial

during mission

Separation I km) 91.6

218.7 239.0

20.8 9-135

96.9 1-160 198.1

85-240

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....... Illlll

Monitor Day Stations 276 Metcalfe

MaUor,/town Metcalfe

278 Mallorytown Telesco~

289 Metcalfe Mallorytown

Table 2: I

Satellites Tracked

I l l

Block I: 6,9,12,13 Block]I: 2,14

II I

Observation Schedule

Observation Span

2 h 54 m

BlockI: 6,9,11,12,13 Block II: 2,14

Block I: 6,9,11,12,i3 Block II: 2,14,15

IIII II

2 h 24 m

2 h 18 m

Comments

Power lost at Metcalfe (data not recovered )

No problems

Power lost at Metcalfe (third of data recovered)

however, in general simultaneous data from five or six satellites were recorded. Some Block II satellites were tracked, however Selective Availability was not on during the campaign.

Figure 3 shows the Geometric Dilution of Precision (GDOP) computed from the Day 278 Metcalfe data. It can be considered to be essentially the same for the other stations and other test days. The GDOP varies from 2.3 - 5.5 during the observation span. Spikes in the plot are due to temporary loss of tracking to some satellites (this will not necessarily occur at all stations). In general, the geometry weakens over the course of the data span, which is due to a reduction from seven to four in the number of satellites tracked.

Data Processing Scheme Data were processed using the SEMIKIN

program, developed at The University of Calgary (Cannon,1990), for precise static and kinematic differential positioning. This program uses the double difference approach where static

10.

8.

6 . 1~

0

482000 | I '1 '11'11'''''' " ! I ' ' !

484000 486000 488000 490000 492000 GPS Time (s)

Figure 3: GDOP of Observation Span

data are processed using a batch least squares adjustment and kinematic data are processed in a Kalman filter algorithm. It has been previously demonstrated that estimated positions from the SEMIKIN package agree with manufacturer- supplied programs such as Trimble's TRIMMBL and Ashtech's KINSURVY, see McLellan and Cannon (1989) and Cannon et al. (1990).

Data from each monitor receiver were processed independently. The data at the initial static baseline were used to estimate the carrier phase double differenced ambiguities while the monitor and remote stations were held fixed. Ambiguities were not fixed to integer numbers, but instead were estimated to be real numbers as is generally the case for ambiguity resolution on long baselines. During the kinematic section of the run, the remote receiver's position and velocity are estimated by the Kalman filter. Corrections to the initial ambiguities were estimated during the kinematic phase to allow for small errors in the ambiguity resolution. Due to the changing monitor-receiver separation during the mission, the magnitude of residual errors caused by incorrectly resolved ambiguities is constantly changing and therefore generates drifts in the solution. Pseudorange, carrier phase and Doppler frequency (phase rate) data were used to update the Kalman filter. Since all measurements are double differenced, no clock states are estimated. More information on the processing methodology can be found in Cannon (1990).

Carrier phase cycle slips were detected using a phase velocity trend method. The phase measurement at the current epoch is predicted using the previous phase measurement and the measured Doppler frequencies, i.e.

~)k+l = ~ k + ~ k + l + qb,,k 2 At (1)

where ~k+l "-" is the predicted phase measure- ment at tk+ 1

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I ) k ®~°

(~)k+ 1 °'"

and At ...

is the phase measurement at t k

is the Doppler frequency measurement at tk+ 1

is the Doppler frequency measurement at t k

is equal to tk+ 1 - t k.

For the present data set, At is 0.5 seconds. The predicted va lue , ~k+l, is then compared to the measured phase observation. If they agree within a certain tolerance, it is assumed that the data are free of cycle slips. The tolerance is determined from the vehicle dynamics and raw data rate. The velocity trend method is very accurate for the monitor receiver (i.e. 1 cycle) since it is stationary and Equation (1) assumes constant phase acceleration over At. This is the case in stationary mode since the Doppler frequency is due to the satellite motion only. Due to the vehicle dynamics at the moving receiver, however, the tolerance may be several cycles to account for the phase acceleration over At. The tolerance is determined through an empirical analysis of the data for various vehicle trajectories. If cycle slips are detected, a new ambiguity is estimated in the Kalman filter "on the fly", i.e. from the kinematic data. This ambiguity is estimated as a real number since the integer ambiguity cannot be instantaneously recovered over these lengths of baselines (generally > 50 km). Over time the ambiguities will converge to a mean value which will be a real number due to residual orbital and atmospheric errors.

Tropospheric corrections were applied to the raw data using standard meteorological parameters. Corrections for the ionospheric effect were not applied due to the lack of L2 data from the aircraft receiver and also because the current version of the Ashtech receivers do not store the broadcast ionospheric coefficients as part of the raw data. However, since L2 squaring data were recorded at the various monitor stations, these data can be used to calibrate the ionospheric effect for the aircraft receiver to reduce the residual effect. This was not attempted in the data post- processing. A cutoff angle of 5 degrees was used.

The estimated position and velocity of the aircraft antenna from the Kalman filter were output to a disk file for each measurement epoch. Corresponding standard deviations were also stored in the output file. A final batch solution using the short static data span at the end of the

mission was performed using the Kalman filter coordinates as a priori values in the least squares adjustment.

Analysis of Results

Assessment of Data Quality

The quality of the GPS carrier phase data was assessed by the number of cycle slips detected. If numerous cycle slips are to be detected on a particular satellite, it may be beneficial to exclude data from that satellite in the final post- processing.

Table3: Cycle Slips Detected in GPS Carrier Phase for Simultaneous

Kinematic Data

Day Station ! Mallorytown

276 Aircraft I

Metcalfe

278 Mallorytown

Telescope Aircraft Metcalfe

Mallorytown

289 Aircraft

Satellite 6

6

6 14 14 12 15 6 15

GPS Time (s) 316470

488700

I

227130 220140 221820 221852 225300 226020 226650

A preliminary data run through the cycle slip detection algorithm and Kalman filter was used to assess the data quality and as Table 3 indicates, in general there were very few cycle slips in the raw data. The table lists the epochs in which cycle slips were detected in the simultaneous kinematic data. On Day 276, a cycle slip on satellite 6 was detected but none occurred at the aircraft receiver, whereas on Day 278 an isolated cycle slip, also on satellite 6, was detected. The lack of cycle slips at the aircraft is a good sign considering the dynamics during the mission. On Day 289, several cycle slips were detected in the aircraft data, however, only one satellite was affected at any one epoch. In general, the data are relatively clean so no satellite data were eliminated during the final post-processing.

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Analysis of Traverse Closures The first analysis of the GPS data is a check on

the traverse closure. Since the aircraft started and finished at the same airport point, once offsets have been accounted for, a measure of the quality of the estimated positions between the start and end of the mission can be gained through closure analysis.

The closure results are the differences in the estimated airport coordinates at the end of the mission using the GPS data and the given control coordinates of that point. A least squares solution of the static data at the end of the run was used to obtain the final coordinates of the ground point. Table 4 summarizes the traverse closures for each test day and each monitor station. Shown are the misclosures in latitude, longitude and height as

well as the RMS closure, i.e. ~/A~2 + A~2 + Ah 2. The error expressed in ppm with respect to the initial baseline length is also given.

In general, the closure results are good, below 2 ppm for three of the five cases. Since the data from Metcalfe and Mallorytown on Day 278 did not have any cycle slips, good traverse closures would be expected at these stations. Also, the Mallorytown data on Day 276 had only one detected cycle slip so the closure can be expected to be accurate.

The traverse closures for Telescope (Day 278) and Mallorytown (Day 289) are larger than for the other data sets. Reasons for this may be the long monitor-remote separation in the case of the Telescope data, and the numerous cycle slips in the case of the Mallorytown data. When the monitor-remote separation is long, it is more difficult to resolve new ambiguities "on thefly", especially using C/A code data. Also, as more cycle slips occur in the carrier phase data, the probability that new ambiguities will not be resolved properly is higher.

Consistency of Position and Velocity Results

The second stage of the GPS analysis is a comparison between the aircraft's position and velocity as determined from various monitor stations. Since no independent control was available to compare with the aircraft's position estimated from GPS, only a consistency check between monitors could be performed. On Day 276, only data from Mallorytown were processed, due to power failures at Metcalfe, so a kinematic analysis could not be performed with these data.

Table 5 summarizes the statistics between various kinematic GPS solutions from different monitor stations. An analysis of both position and velocity are given. For Day 278, the number of comparisons (i.e. sample size) is about 14,000 while it is about 4000 for Day 289 due the power failure at Metcalfe part way through the run. Since the measurements at the monitor stations and the aircraft were simultaneous, a direct comparison of the aircraft positions determined from the various monitors could be made and no interpolation was necessary.

The mean velocity differences for each of the comparisons is zero, showing that no systematic effects occur in the estimated velocities from each monitor station. RMS values are below 1 cm s -I in the north and east directions and below 2 cm s "1 in height. This agreement is good considering the distance between the monitor stations. Plots of velocity differences in horizontal and vertical direction between the Metcalfe and the Mallory solution on day 278 are shown in Figures 4 and 5. They are typical for all the other runs. There is no apparent long-term variation about the zero mean in either plot but there is a distinct increase in the magnitude of the differences for the vertical velocities. Velocity results are much more consistent than position results since they

Day

276

278

289

Table 4: Traverse Closure Results Station A~ A~, Ah RMS

Mallory, town

Metcalfe

Mallorytown

Telescope

MallorTtown

(cm)

12.7

2.2

(cm)

4.6

3.3

(cm)

-6.1

0.3

(cm)

14.8

4.0

ppm

1.5

1.9

-9.4 12.7 0.8 15.8 1.6

125.1

-14.8

18.5

60.2

197.7

68.4

-152.0

-28.8

9.9

7.1

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0.06

u 0.04

~0 .02 e

o.oo

-0.02 0

-0.04

-0.06 4830O0

I ' I J I '

485000 4 8 7 0 0 0 4 8 9 0 0 0 491000

Time (sec)

Figure 4: Velocity Differences in East Direction Between Aircraft Solutions from

Metcalfe and Mallorytown, Day 278

are not directly affected by incorrect ambiguity resolution and cycle slips. In addition, long-term systematic position differences as indicated by non-zero means in Table 5 have very little influence on velocity. The velocity results represent the consistency of the GPS solutions. Common mode systematic effects in the aircraft velocity are eliminated by differencing between the two trajectories. However, results from independent tests with good outside control confirm the RMS values given above; see Schwarz (1991).

Table 5: Aircraft

Day Monitors

Metcalfe -

Mallorytown

Metcalfe -

278 Telescope

Mallorytown

- Telescope

Metcalfe - 289 Mallorytown

0.06

"~0.04

~ 0 . 0 2

~o.oo

o.o2 O

~0.04

-0.06 483000 4 8 5 0 0 0 4 8 7 0 0 0 4 8 9 0 0 0 491000

Time (sec)

Figure 5: Velocity Differences in Vertical Direction Between Aircraft

Solutions from Metcalfe and Mallorytown, Day 278

The quality of the kinematic position results is a function of the separation between the aircraft and the monitor stations. For example, the Metcalfe- Mallorytown root mean square (RMS) results on Day 278 are better than 20 cm in all three coordinates. However, the mean values are not close to zero for the latitude and longitude components, indicating the presence of systematic effects. The source of the systematic effects is discussed in the sequel. On Day 289, the Metcalfe-Mallorytown RMS results are poorer in

Kinematic Results

Position Velocity Mean RMS Std dev Mean RMS

Coord (cm) (cm) (cm) Direct (cms.1) (cms.1)

(~ 9.1 11.3 6.7 north 0.0 O. 8 10.5 11.5 4.7 east 0.0 0.6

h O. 4 17.6 17.6 height O. 0 1.8

(~ -49.8 60.8 34.9 north 0.0 0.8 ~, 5.3 10.9 9.5 east 0.0 0.5 h 22.2 46.0 40.3 height 0.0 1.7

(~ -58.9 70.0 37.8 north 0.0 0.8 ~, -5.3 11.9 10.7 east 0.0 0.5 h 21.8 53.4 48.7 height 0.0 1.7

i

-4.0 8.6 7.6 north 0.0 0.7 ~, -9.9 10.9 4.6 east 0.0 0.6 h 47.9 52.0 20.2 height 0.0 1.4

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the height component (52.0 cm) but good in the horizontal components. Again, there are large systematic effects in the results, especially in height where the mean difference is 47.9 cm. In all cases, the elimination of the non-zero mean results in standard deviations of less than 10 cm in the horizontal components and 20 cm in height.

Comparisons with the positions derived from the Telescope monitor station are significantly poorer than the Metcalfe-Mallorytown case. This is to be expected since Telescope is relatively far away from the other monitor stations• In this case, the RMS statistics range from 11 - 70 cm with large mean values. It is interesting to note that the results from Metcalfe - Telescope are very similar to the Mallorytown - Telescope results. This is due to the similar geometry from the Metcalfe and Mallorytown stations since they are closer together.

Figure 6 shows a plot of the differences in the aircraft latitude determined from Metcalfe and Mallorytown vs the difference in distance between Metcalfe and the aircraft and Mallorytown and the aircraft. The latitude differences contain a systematic effect as does the longitude and height components, shown in Figures 7 and 8. Figure 9 shows the latitude differences between Mallorytown and Telescope for Day 278. In this case, the mean difference is -58.9 cm and the RMS is 70.0 cm (see Table 5). As can be seen from the figure, a large systematic effect is

30 Lat Diff

- - ~ ............ Separat ion

i

2O

10

0

A.

, : , Vj i I t , | • • • g vi '!i

| ,:

I ' I ' I '

484OOO - 1 0 . . . . . . . . i

482000 486000 4880OO 490OOO

GPS Time (s)

100

50

0 "~

or3

-50

, -100 4 9 2 0 0 0

Figure 6: L a t i t u d e D i f f e r e n c e s B e t w e e n A i r c r a f t S o l u t i o n s f r o m M e t c a l f e a n d

M a l l o r y t o w n , D a y 2 7 8

30 100

- t - . .K...

f ' ! 20 : i 50

f .

.-I ; ! : ¢~0

, ; [ • • |

! | ! , . : 0 i.. . "i : -50 ,.: ; . . . . . .,- "! : t ; : V i : i :"

L o n D i f f ( c m ) ; j i .: .............. Separat ion (km~ rd t ,~

-10 " , • , • , • , • -100 482000 484000 486000 488000 490000 492000

GPS Time (s)

Figure 7: L o n g i t u d e D i f f e r e n c e s B e t w e e n A i r c r a f t S o l u t i o n s f r o m

M e t c a l f e a n d M a i l o r y t o w n , D a y 2 7 8

present in the difference plot. Near the end of the run, the differences increase as a function of the changing baseline separat ion. The large systematic effects are most likely due to residual atmospheric and orbital effects as well as incorrectly resolved initial ambiguities.

80

60

4O

20

-20

-4O 482000

Hg t Diff .............. Separat ion

J I " I J I • ...... i

484000 486000 488000 490000

GPS Time (s)

100

5O A

@ 0 =

¢ d

' -50

, -100 492000

Figure 8: H e i g h t D i f f e r e n c e s B e t w e e n A i r c r a f t S o l u t i o n s f r o m M e t c a l f e a n d

M a l l o r y t o w n , D a y 2 7 8

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-50

-g

.150"

300

M.

"~ ~ LatDif f Separation

| I l i

486000 488000 490000 -200 -100

482000 484000 492000

200

loo.

or] 0

GPS Time (s)

Figure 9: Latitude Differences Between Aircraft Solutions from Mallorytown and

Telescope, Day 278

Table 6: Standard Deviations of the GPS-INS Coordinate Differences

Flight Line

zapl06104

zapl06106

zapl06108

zap 1061 I0

zap106112

Std dev (cm) 0.011

0.007

0.013

0.038

0.028

Std dev ~, (cm)

0.007

0.006

0.011

0.013

0.010

Std dev h (cm) 0.054

0.011

0.019

0.018

0.064

zap106205 0.014 0.024 0.056

zap106207 0.010 0.009 0.035

zap106210 0.018 0.013 0.034

zap106212

zap106214

0.054

0.060

0.035 0.067 i

0.083 0.081

Comparison with INS results

INS data were used to check the reliability of the phase velocity trend method to detect cycle slips and to get an independent estimate of aircraft velocity. Due to the discontinuities in the data collection, no solution for the whole INS trajectory could be obtained. It was therefore necessary to initialize and update each part of the INS trajectory using GPS positions. This means that only relative comparisons are possible, i.e. velocity variations between GPS updates can be compared. A Kalman filter was implemented to integrate horizontal velocities and the quantized vertical accelerations obtained from the LTN 51 and to obtain a convenient algorithm for GPS updates. The type of data output is peculiar to this system and required some changes in the standard algorithm. They are discussed in more detail in Schwarz et al (1991b).

To test the reliability of the GPS cycle slip correction algorithm, INS velocities were integrated over the half second interval between GPS data epochs. The coordinate differences derived in this way could directly be compared to the coordinate differences obtained from GPS. Agreement should be at the noise level, i.e. at the level of a few centimetres. Table 6 shows that this is indeed the case. The RMS agreement is at the level of 1 to 3 cm for good data sets and below 5 cm in most other cases. Any discrepancy considerably larger, say at the level of 10 cm or above, can therefore easily be detected. Analysis of the flight lines showed no evidence of uncorrected cycle slips. Thus, cycle slips were detected and corrected reliably by the phase velocity trend method. There were, however, a number of incidences where spurious data peaks in individual GPS positions were detected. They were typically between 10 and 50 cm and there is no obvious explanation for the source of these errors.

Comparison of the raw INS velocities and the GPS derived velocit ies show short-term systematic errors with amplitudes of about 5 cm and periods of about 30 s. They may be due to a variety of reasons, of which uncompensated orientation changes of the offset vector between INS centre and GPS antenna is a likely explanation. If these systematic components are eliminated by an auto-regressive (AR) process, the residual rms differences between the INS and the GPS velocities are at the level of 2 to 3 cm s -1 for the horizontal and 3 to 6 cm s -1 for the vertical. This agrees well with the positioning results. It also indicates that the noise level of the INS system is about 2 cm s -I for the horizontal

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velocities and 3 to 5 cm s -1 for the vertical velocity, assuming that the previous estimate of the GPS velocity is correct.

Conclusions and Recommendations

The CCS/CCRS airborne tests conducted in October 1990, provided an extensive data set to investigate the consistency of differential aircraft positioning from several monitor stations. Traverse closure results are generally satisfactory, within 2 ppm when baselines are below 100 km and only isolated cycle slips occur. This shows that the model and cycle slip detection algorithm are working well. For cases where these conditions are not met, results are poorer by a factor of five.

The consistency of GPS kinematic positioning from various monitor stations is dependent on the separation between the monitor stations and the aircraft. Results show that the agreement between kinematic solutions is better than 20 cm for the case of Metcalfe vs Mallorytown (Day 278) when no cycle slips are detected. This agreement decreased to 50 cm in the height component when several cycle slips were detected (i.e. Metcalfe- Mallorytown, Day 289). When a very long baseline is used in the consistency check (i.e. Metcalfe-Telescope, Mallorytown-Telescope), the agreement decreases accordingly, e.g. to 11 - 70 cm RMS. This is mainly due to unmodelled systematic effects and incorrectly resolved ambiguities.

The consistency of velocity determination using GPS is excellent. No degradation in the velocity accuracy due to baseline length was detected when solutions from various monitor stations were compared. RMS agreement between the solutions was at the 0.8 cm s -1 level in the horizontal com-ponents and 1.8 cm s -1 in the vertical component. The meaz~ differences were 0.0 cm s -1 in all cases indicating that no systematic effects were present in the estimated results. Cycle slips do not affect velocity determination since the Doppler frequency observable is a direct measure of the phase rate and is not dependent on the carrier phase ambiguity.

Comparison with simultaneously collected INS data showed that the GPS cycle slip algorithm used in this analysis works reliably . The comparison also gave an independent confirmation of the GPS derived velocity results. It showed some spurious peaks in the GPS-INS differences which are clearly due to the GPS data stream and need further investigation.

In terms of recommendations for future airborne GPS tests, it would be beneficial to use P code GPS technology. If the quoted manufacturer's specifications can be met (i.e. 10 cm P code noise), this would greatly enhance the ability of GPS to accurately resolve carrier phase ambiguities "on thefly". Ionospheric corrections could also be applied, since the ionospheric errors are significant over these monitor-remote separations. For the current case of L2 squaring data being collected at the monitor stations, a model to determine the ionospheric effect at the aircraft could be investigated.

If INS is again used as an independent data source, a system with an adequate recording device is recommended, so that the full INS trajectory can be determined independently. For ease of operation and processing, a navigation grade strapdown system should be considered.

An additional recommendation would be to increase the number of monitor stations so that at least one initial monitor-remote separation would be very short, say a few km at most. Using this strategy, the initial cartier phase ambiguities could be correctly resolved to their integer values and an higher accuracy aircraft solution could be estimated.

Acknowledgements

Funding for this project has been supplied by CCS and CCRS. This is gratefully acknowledged. Assistance from Dr. Jack Gibson in the reduction of the INS data is also appreciated.

References

Cannon ME (1990) High-Accuracy GPS Semikinematic Positioning: Modeling and Results, Navigation, Vol. 37, No. 1, pp. 53-64.

Cannon ME and KP Schwarz (1990) A Discussion of GPS/INS Integration for Airborne Photogrammetric Applications, Proceedings of the International Symposium on Kinematic Systems in Geodesy, Surveying and Remote Sensing, KIS1990, Banff, Alberta, Canada, September 10-13, pp. 443-452.

Cannon, ME, G Lachapelle, KP Schwarz and H Ayers (1990), Comparison of SEMIKIN and KINSURVY for Kinematic Applications, Proceedings of the Third International Technical Meeting of the Satellite Division of the ION, GPS-90, Colorado Springs, September 11-13.

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