Small Aperture Telescope Observations of Co

Small Aperture Telescope Observations of Co-located Geostationary Satellites
Robert (Lauchie) Scott
Defence R&D Canada -Ottawa, Ottawa, Ontario, Canada, K1A 0Z4, robert.scott@drdc-rddc.gc.ca
Dr. Brad Wallace
Defence R&D Canada -Ottawa, Ottawa, Ontario, Canada, K1A 0Z4, brad.wallace@drdc-rddc.gc.ca
ABSTRACT
Co-location is a geostationary orbit formation strategy where two or more satellites reside within one station keeping box.
As geostationary orbit continues to be populated, satellite operators are increasing usage of co-location techniques. Colocation causes the participants to move in relative motion ellipses about each other with typical separations varying from 1
to 100 kilometres. This paper focuses on correlation effectiveness on co-located geostationary satellites as their close
proximity to one another is a challenge for ground-based space surveillance sensors. During the course of this study we
identify two unique observational events where co-located satellites’ close proximity causes problems for ground based
sensors. The satellites sometimes appear to conjunct which makes discrimination by automated space surveillance systems
difficult. During these conjunctions, if one of the satellites is more optically reflective than the other, the possibility exists
that it will glint-mask the fainter satellite under small phase angle conditions, further making its detection difficult.
1. I&TRODUCTIO&
An issue encountered with automated optical space surveillance systems is satellite identity discrimination while observing
closely-spaced geostationary (GEO) satellites. These systems can unintentionally mistag the identity of the objects they are
tracking due to uncertainties in the predicted locations of the satellites. A more acute case is satellite orbital co-location
which is gaining popularity with satellite operators to populate more capacity in geostationary orbit. Co-location is a GEO
orbit formation strategy where two or more satellites reside within one geostationary longitude box [1]. As of this writing
there are 48 satellites in 22 co-located groupings in GEO. Canada currently co-locates 6 satellites as 3 co-located pairs. The
Luxembourg operator, SES, once co-located six satellites [2] in one longitude slot and currently has 3 co-located clusters.
The satellites are often separated by less than 0.05 degrees of longitude and this close proximity between the satellites
strains the accuracy of the general perturbation orbital elements used to predict their orbits and to correlate the identities of
the satellites in optical systems.
In a more extreme case, co-located satellites sometimes appear to conjunct. The actual physical separation may be tens of
kilometers or more (more than enough to safeguard against collision) but the apparent angular extent of the satellite
separation is small when observed by a ground-based observer. Under certain conditions it can become impossible to detect
two individual objects on the field of view as the two satellites appear to merge on the image plane. For large geostationary
satellites, CCD (Charged Couple Device) imager saturation is a possibility as the combined visual magnitude of the objects
can sometimes exceed the well depth of limit of an optical CCD sensor performing metric measurements. In another case,
two co-located satellites with largely different optical cross sections can cause glint-masking effects which can effectively
mask the presence of the other satellite on a CCD imager.
This paper seeks to characterize correlation effectiveness of a Ground Based Optical Sensor on co-located satellites using
the DRDC Ottawa Space Surveillance Observatory [3]. The following sections provide a brief overview of geostationary
orbital parameters and how geostationary co-location is performed. Relative motion of co-located satellite motion is
described, and an analysis of measurements taken by a small ground based telescope is then presented. A check of the
effectiveness of the correlation approach used to differentiate the satellites is investigated and results presented. We show
that correlation is practical with relatively current orbital element sets on these objects but changes to the existing
correlation algorithm in use by DRDC is required to further enhance its accuracy. Special cases are also shown where colocated satellites perform unique space surveillance events when detected by CCD imagers. Co-located satellites result in
unique observational events as well. A visual conjunction (objects appearing to merge on the CCD imager) and glintmasking event where one object overwhelms the other due to one objects’ high reflectivity were both detected on closeformation co-located satellites.
2. GEOSTATIO&ARY SATELLITES A&D CO-LOCATIO&
The International Telecommunications Union (ITU) assigns geostationary orbital longitude slots to satellite operators to
maintain frequency management of the GEO orbit resource [4]. The ITU typically assigns a station keeping box of ~0.1
degrees in longitude and latitude, with approximate dimensions being 74 x 74 x 35 km. In practice, new geostationary
satellites maintain station within 0.05 degrees of longitude and latitude and limiting dead bands are used to help prevent
orbital box violation.
Latitude (β)
~0.1°
Nimiq-1
DirectTV-1
Stationkeeping
box 2 (unused)
Galaxy-11
Long (λ)
Co-location
Fig. 1 Geostationary satellite station keeping boxes with imagery showing Galaxy 11 (left on image) and the
co-located *imiq-1, Direct TV-1 group (center of image)
Geosynchronous orbital elements are linearized [1] Keplerian elements used for describing near circular geostationary
satellite orbits. The primary parameter of the geostationary orbit is the semi major axis parameter (aGEO) which is
equivalent to 42,164.5 km. The offset of the geostationary satellites orbital semi major axis size (a), above or below aGEO is
expressed as equation 1.
(1)
δa = a − a
(a ≡ 42,164.5 km )
GEO
GEO
Since the orbital inclination and eccentricity are near zero, the eccentricity and inclination vectors can be expressed as
projections onto Earth’s equatorial plane as equations 2 and 3 respectively.
[
T
e = [e cos(ω + Ω), e sin(ω + Ω)] = e x , e y
]
T
[
T
i = [i cos(Ω), i sin(Ω) ] = i x , i y
]
T
(2)(3)
Where e and i are the eccentricity (dimensionless) and inclination (degrees) parameters for the geostationary orbit
respectively. The parameter ω is the argument of perigee and Ω is the orbit’s right ascension of the ascending node. The
eccentricity vector points towards the location of orbital perigee and the inclination vector is a projection of the
geostationary satellite's inclination vector onto the equatorial plane. The longitude drift rate (D) of a geostationary satellite
with a semimajor axis above of below that of aGEO is expressed as equation 4.
D = −1.5
δa
(4)
aGEO
The co-location of two or more satellites in the same geostationary longitude slot requires management of the risk of
collision between the co-located GEO peers. This encourages a separation approach for the formation. Geostationary
satellites with suspended solar panels that experience an impact with as little as 1 m/s velocity could be very destructive
[1]. The separation strategy normally used for co-located satellites uses combined offsets between the pair’s eccentricity
and inclination vectors [1][5]. A relative motion ellipse is then formed where in-track position uncertainties of the satellites
are aligned nearly perpendicular to the relative motion of the two spacecraft (fig. 2). This elegantly places the much higher
precision components of radial and cross-track uncertainty in a direction that helps maintain separation between the
satellites.
The ellipse's shape can be estimated by determining the differences between each satellite's geostationary elements. The
eccentricity and inclination vector differences, eij and Iij are shown as equations 5 and 6 respectively. These parameters
establish the geometric shape of the relative motion ellipse.
eij = ei − e j
∆I ij = ii − i j
(5)(6)
The two satellites are forced to stay within close proximity to one another therefore the relative drift rate between the
satellites is near zero (equation 7). In practice, real orbital operations incur maneuver errors and small drift rates are
observed but the drift tends to be small between the co-located members. Hence a longitude offset between the two colocated satellites is preserved between the spacecraft.
Dij = Di − D j ≈ 0
(7)
A
In-track
cross-track
Apparent
group orbit
A
radial
B
B
Co-located GEO orbits with
inclination and eccentricity
offsets
Apparent
object motion in
cluster
Apparent orbit and apparent
relative motion inside the colocated group.
Fig. 2. Colocated satellite motion.
As the satellites are in near circular orbits, the size of the relative motion ellipse can be estimated by computing the
following radial, in-track and crosstrack values relative to an arbitrary "primary" satellite in the co-located group. It should
be noted that in normal space surveillance tasking, one object is tracked when tasked and the telescope tracks the tasked
object, not both satellites in the co-located group. Therefore, the other co-located member would appear to move relative to
the “tasked” satellite during tracking.
(8)
∆RadialGEO = 2aGEO eij
∆Intrack GEO = 4aGEO eij
∆Crosstrack = aGEO I ij
(9)
(10)
The in-track ellipse size is twice the radial separation size due to the eccentricity offset [6]. The size of the relative motion
semi minor axis can be approximated by equation 11.
2 CAsize = aGEO 20 eij + I ij
(11)
2
∆Crosstrack
∆Crosstrack
∆Radial
∆Intrack
2D
Fig. 3.Relative motion ellipse radial, in-track and crosstrack motion directions.
The separation between the spacecraft is the linear distance using the separation of longitudes λij is expressed as equation
(12)
(12)
IS = a λ
GEO
ij
The satellite peer can appear to "orbit" a location not around the primary satellite if there is considerable difference
between the mean longitude of the satellites. This usually takes place if the longitudes of the satellites are offset by a
distance larger than the ∆in-track size. A useful ratio to identify this is the ratio of the longitude separation to the in-track
ellipse size, termed IR.
IR =
IS
∆Intrack GEO
=
(13)
λij
4 eij
IR ratios greater than 1 indicate that the satellites undergo collocated motion where the relative motion ellipse forms
externally to the primary object. IR ratios less than 1 indicate that the peer orbits about the primary satellite.
3. CA&ADIA& GEO SATELLITE CO-LOCATIO& GEOMETRY SUMMARY
Canadian geostationary co-located satellite geometry is shown in Fig 4 and Fig 5. It is seen that the co-located Canadian
satellites are in relatively tight formation where the relative motion ellipses are ~20-120 km in size. The co-located motion
has in-track (IR) ratios less than one hence the objects tend to normally orbit the primary satellite. Note that Direct TV-3
was repositioned in March 2009 which shows as growth in the ellipse size at that time. A 20 km separation at 40,000 km
range is approximately 103 arcsecond as observed from the ground.
Relative Motion Ellipse Size
Co-located Canadian Satellites (2008-2009)
200
Anik F1, Anik F1-R
Nimiq-2, DirectTV-3
Nimiq-1, DirectTV-1
Relative Motion Ellipse Size (km)
180
160
140
120
100
80
60
40
20
0
Nov 2007
Feb 2008
Jun 2008
Sep 2008
Dec 2008
Mar 2009
Jul 2009
Date
Fig. 4.Relative motion ellipse size for co-located Canadian satellites. Most objects maintain 20km and greater
ellipse sizes.
Intrack Ratio
Co-located Canadian Satellites (2008-2009)
5
4.5
Anik F1, Anik F1-R
Nimiq-2, DirectTV-3
Nimiq-1, DirectTV-1
IR Ratio (dimensionless)
4
3.5
3
2.5
2
1.5
1
0.5
0
Nov 2007
Feb 2008
Jun 2008
Sep 2008
Dec 2008
Mar 2009
Jul 2009
Date
Fig. 5.In-track Ratio (IR) for co-located Canadian satellites. Most Canadian objects "orbit" around the other
satellite in the co-located group.
4. GROU&D BASED OPTICAL CORRELATIO& ALGORITHM
The Ground Based Optical Correlation algorithm was designed by DRDC Ottawa to minimize mistags on deep space
objects which fall into clustered groups. When an image is processed by the image processor, a snap propagation of the
entire SSN (Space Surveillance Network) elset catalog is performed over the time span of the image being processed. Each
object detected in the image is assigned a coordinate (RA,DEC) pair and objects falling within a 1 degree angular radius are
considered as candidates for tagging. The algorithm then performs a nearest neighbour matching, where objects with the
smallest angular separation between measured and predicted locations are selected. High confidence matches are normally
found when the match is found within 400 arcseconds between detected and predicted object locations. Each object
detected in the CCD image is tested against the predicted satellite positions independently (Fig 6.)
+ 19751
+ 22930
+ 25740
+ 26038
+ 26620
Fig. 6.Correlation processes (not to scale) - each detected object is checked against the predicted positions (red crux) of
nearby objects in the SS* catalog. In this case, 25740 (*imiq-1) would be tagged as it is the nearest-neighbor. 19751 is not
considered as it is external to the 1degree candidate radius.
5. CO-LOCATED SATELLLITES OBSERVED
Data for this study was collected using the DRDC Space Surveillance Observatory [3] located in Ottawa, Ontario. Images
were collected in rate-track mode where the telescope tracks the satellites at the rate which they move across the sky. Once
each 4-second exposure image is acquired, it is immediately transferred to DRDC Ottawa for automated processing by the
Semi Quick Intelligent Detector (SQUID) [3] image processing algorithm.
The Canadian geostationary satellites observed and their co-located peers are listed in table 1. Data were collected on 24,
25 February 2008 and 17 Mar 2008
Table 1. Co-located Canadian Satellites and Peers
Primary
Nimiq 2 (27632)
Nimiq 1 (25740)
Anik F1 (26624)
Co-located Peer
Direct TV-3 (23598)
Direct TV-1 (22930)
Anik F1-R (28868)
Long. (deg °W)
82.0
91.2
107.3
6. MEASUREME&TS
All datasets have data blackouts where the geostationary satellites entered eclipse. The data is displayed using Right
Ascension as the primary coordinate between all measurements. Right ascension clocks proportionally with time as the
GEOS orbit the Earth thus, right ascension serves as a convenient independent variable when combining brightness and
position plots for co-located satellites.
Examples of the data analysis are shown as Figs 7-9 and show visual magnitude of each satellite, followed by declination
and SGP4 residuals. The declination plot shows the measured position of the tagged satellites. Conjunction locations are
implied where the two declination curves cross each other. This location does not necessarily indicate that the two satellites
are occulting each other as they may be separated by a small angle in right ascension. The last plot is the residuals between
the measured J2000 satellite positions relative to the predicted topocentric SGP4 (Simplified General Perturbation)
position. This information is a relative indicator of the quality of matching between the element set and its actual measured
position on the sky.
Small insets are also shown on each figure with reference predicted positions of the observed satellites indicated by the
yellow dot. The reference predicted positions are propagated using SGP4 and plotted using TheSky6TM software. The star
streaks in each frame are a relative indicator of angular size. As each exposure was 4 seconds duration, the streak length is
approximately 60 arcseconds. Each plot has the true identity and position of each satellite marked as a callout to help
identify which object is which on the plots and on the image insets.
7. DATA A&ALYSIS
The percentages of correct tagging of the co-located satellites for the primary and secondary objects are shown as
percentages in table 2. The colour indicators indicate the relative success of the correlation algorithm. Green is good
performance (>90%) tagging, yellow (50% - 89%) moderate and red indicating datasets that were tagged with less than
50% accuracy.
There is a weakness in the nearest neighbour correlation algorithm when the predicted satellite positions are offset, in-track,
from the measured positions of the satellites (Fig. 10). This caused entire datasets to be mistagged during the tracks on the
Nimiq-1 and Direct TV-1 group (e.g. Fig. 8). The correlation algorithm assigned the nearest object's identity (25740) to
both objects as it was the nearest object to detections. In general, it was observed that the measured satellite positions are
offset by ~100 arcseconds or more in-track of the predicted positions.
The average age of the reference orbital elements used for correlation averaged 2.4 days. A small bias appears as the elsets
for 24 and 25 February 2008 were unchanged as no new elsets were published at that time. The lowest aged elset, Anik F1R on 17 March, had peak correlation size of 40 arcseconds showing excellent agreement with the measured positions of the
satellites. A weak dependence on elset age and maximum correlation residual was observed where the difference between
measured position and elset position changed by ~40 arcseconds per day of elset age.
Table 2. Ground Based Optical Correlation Performance on Co-located Canadian Satellites
Peak
CA Size Intrack
IR
Tagging
Reference
(km)
separation
Effectiveness Elset Age(d) Separation
before Mistag
(km)
(Primary
(arcsec)
/secondary)
&imiq-2 / Direct TV-3
24 February
24%
91%
3.0
1.7
77
171
38.1
-10.3
0.3
25 February
100% 73%
4.0
2.7
107
194
19.8
-15.4
0.9
17 March
100% 100% 1.7
2.7
80
24
24.9
-16.9
0.8
&imiq-1 / Direct TV-1
24 February
93%
8% * 1.8
2.6
196
101*
48.2
16.2
0.4
25 February
100%
0% * 3.6
3.6
209
98*
48.2
16.9
0.4
17 March
100%
0% * 1.6
1.6
82
139*
50.1
25.0
0.6
Anik F1 / Anik F1-R**
24 February
70%
100% 1.8
2.8
107
79
45.3
-10.3
0.2
25 February
76%
100% 2.8
3.8
74
110
45.2
-11.0
0.3
17 March
99%
100% 1.6
0.6
70
40
18.1
-3.7
0.2
* indicates object was correlated with wrong element set
** SSN catalog has suspected embedded mistag where Anik F1 and Anik F1R have been swapped
2008
Observations
Correlation Effectiveness
Overall the correlation algorithm performed well as the predicted positions of the satellites were relatively well matched to
the measured positions for the co-located clusters. Often, it was observed however that the algorithm would begin a track
well correlated, but as time progressed, it degraded as the predicted reference satellite’s position lost consistency with the
shape of the co-located cluster (Fig 9). The Nimiq-1 cluster (Fig 8) showed an entire dataset which blanket mislabeled
Direct-TV1 as Nimiq-1. This occurred as Nimiq-1’s predicted position was closest to both satellites for the entire duration
of the tracks. Similar behaviour was also observed in the datasets for Anik F1 and Anik F1R.
A recurring event that was noted was that the elsets were usually offset (Fig. 10) by a lag in right ascension of ~100
arcseconds and ~100 arcseconds or more in declination. The correlation algorithm did not handle this offset very well and
the nearest neighbour approach mistagged both satellites with the nearest object. It is of note that the general arrangement
of the two objects was well preserved, but the offset in right ascension caused the correlation algorithm to fail. A future
iteration of the correlation algorithm is already under consideration which would perform a least squares fit to the residuals
to ignore this offset's effect and preserve the arrangement of the satellites when tagging them.
Nimiq-2, 23598 Photometry
17 Mar 2008
6
9
10
23598
Mv
Cluster peer(s)
Nimiq-2
Direct TV-3
80"
Propagated linear
separation
8
11
27632
12
13
Nimiq-2
14
15
16
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Right Asce nsion
Nimiq-2, 23598 Measured J2000 RA,DEC
17 Mar 2008
-6.74
-6.76
-6.78
-6.80
Declination (deg)
17-Mar 08 00:05
17-Mar 08 10:21
Conjunction
separation (arcsec)
7
-6.82
23598
27632
-6.84
-6.86
-6.88
Nimiq-2
-6.90
-6.92
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Right Ascension (hours)
Nimiq-2, 23598 Measured - SGP4 Positions
17 Mar 2008
450
400
350
300
Residuals (arcsec)
Data
Timespan(UTC)
250
23598
27632
200
150
100
Nimiq-2
50
0
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Right Ascension (hours)
Fig. 7.*imiq-2, Direct TV-3 17 Mar 2009
9.3 km
Data
Timespan(UTC
Cluster peer(s)
Nimiq-1 (25740) , Direct TV-1 (22930) Photometry
25 Feb 2008
Conjunction
separation (arcsec)
6
7
Propagated linear
separation
8
9
Mv
10
25740
11
12
13
14
Nimiq-1
15
16
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Right Ascension (hours)
Nimiq-1 (25740) , Direct TV-1 (22930) J2000 RA,DEC
25 Feb 2008
16.00
14.00
Declination (deg)
12.00
10.00
25740
8.00
6.00
4.00
2.00
0.00
2.00
Nimiq-1
4.00
6.00
8.00
10.00
12.00
14.00
Right Ascension (hours)
Nimiq-1 (25740) , Direct TV-1 (22930) - SGP4 Positions
25 Feb 2008
450
400
Residuals (arcsec)
350
300
25740 AngSep
250
200
150
100
Nimiq-1
50
0
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Right Ascension (hours)
Fig. 8.*imiq-1, Direct TV-1
25 Feb 2008 03:25
25 Feb 2008 07:36
Nimiq-1
Direct TV-1
60"
4.5 km
Anik F1 (26624), Anik F1R (28868) Photometry
24 Feb 2008
6
7
8
Data
Timespan(UTC
17-Mar-2008 00:12
17-Mar-2008 10:21
Cluster peer(s)
Anik F1-R/Anik F1
Conjunction
separation
(arcsec)
Propagated
linear separation
31.5
4.8 km
9
Mv
26624
10
28868
11
12
Anik F1-R
13
14
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
Right Ascension (hours)
Photo acquired at RA=4.5h
Anik F1 (26624), Anik F1R (28868) Measured J2000 RA,DEC
24 Feb 2008
-6.64
-6.66
Declination (deg)
-6.68
-6.70
26624
28868
-6.72
-6.74
Anik F1-R
-6.76
-6.78
-6.80
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
Right Ascension (hours)
Anik F1 (26624), Anik F1R (28868) Residuals
Measured - SGP4 Positions
24 Feb 2008
120
R esiduals (arcsec)
100
80
26624 AngSep
60
28868 AngSep
40
Anik F1-R
20
0
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
Right Ascension (hours)
Fig. 9.Anik F1, Anik F1-R
Fig. 10. Stressing cases for the nearest neighbour matching algorithm. The algorithm would be successful on the
frame on the left, however both objects would be mistagged as Direct TV-3 on the right as it is closest to both
detected objects. An improved correlation approach would need to handle the offset condition that was
frequently seen for the co-located satellites.
Visual Conjunctions and Glint Masking
Anik F1 and Anik F1R maintain a tight co-location formation within ~ 40 kilometres of each other and this
resulted in unique events on 24 and 25 February 2008. Both satellites appeared to close within 20 arcseconds of
each other while the satellites were at minimum phase angle while just exiting Earth’s shadow. When both
objects re-entered direct sunlight, the sensor recorded a merged elongated object and was ignored by the image
processor as a star streak. The site seeing conditions were ~4 arcseconds causing the bright pixels to pass the
image processor threshold, and merged the two objects together.
Fig. 10. Visual conjunction of Anik F1 and Anik F1R. Left – both satellites while they were partially illuminated
in penumbra. Center – objects after exiting Earth’s shadow, Right – Anik F1 and Anik F1R two hours later.
A visual conjunction occurred near minimum phase angle where a glint was observed from Anik F1. The
imagery shows that Anik F1 overwhelmed the signature of Anik F1-R while in close proximity to one antoher
(Fig. 11) and again a large, saturated object was detected along with blooming streaks at ~Mv 5.6. This
effectively masked the presence of Anik F1-R to the SQUID detection algorithm. The saturated object would
provide unreliable metrics as centroiding would be difficult and separating the positions of the two objects is not
possible with the current system. The sun’s declination at the time of observation did not suggest that a specular
glint condition was occurring off the main panels. It is possible that a specular reflection off another part of
Anik F1 was occurring at that time. A possible prevention mechanism of this is to avoid observing these
satellites when their phase angles are below 20 degrees.
7:17 – 39 arcsec separation
Mv 7.6, Mv 9.3 (measured)
7:22:43, 35 arcsec separation Mv 6.0
7:27:55 – 34 arcsec separation, Mv 5.6
Fig. 11. Glint mask event where Anik F1 overwhelms the nearby signature of Anik F1-R.
8. CO&CLUSIO&
We find that the nearest neighbour correlation algorithm using predicted positions of the satellites against
detected positions worked relatively well on co-located satellites. It was noted that there were frequently
recurring offsets between predicted and observed satellite positions and this was not well handled by the
correlation algorithm. Often it is seen that co-located satellites' relative geometry was relatively well matched
with the detected positions of the satellite, but the 100 arcsecond offset caused the correlation algorithm to
mistag both satellites with the closest predicted object’s identity. Inherently, the element sets do a relatively
good job predicting the arrangement of the satellites, but not their precise positions on the image plane. Visual
conjunctions were observed on Anik F1 and Anik F1-R where sensor seeing conditions resulted in elongated
object formation on the CCD detector and the objects were ignored by SQUID. An extreme case of a visual
conjunction was observed where Anik F1 glint-masked Anik F1-R. Anik F1 began reflecting a large amount of
sunlight back toward the sensor while in close proximity to Anik F1-R. The observed, merged, saturated object
was also ignored by the SQUID processor due to blooming streaks and size of the merged object on the detector
plane. The combined visual magnitude of the merged object was in excess of Mv 5.9 and rendered Anik F1-R
undetectable by sensor. Follow-on studies should consider expanding the number of co-located satellites after
adjustments to the correlation algorithm have been performed. Follow on studies should also compare the
tagging effectiveness with both co-located satellites and neighbouring geostationary satellites residing within
their own station keeping boxes. A more advanced detection algorithm to split adjacent satellites during visual
conjunctions would also be of benefit. A useful test of this new algorithm would be to determine the
effectiveness of centroiding two closely spaced, bright objects of different relative intensities to help address the
glint masking issue as well.
9. FUTURE CO&SIDERATIO&S FOR SPACE SURVEILLA&CE
As the geostationary belt further continues to be populated it is likely that co-location will be employed more
often, hence, some of the problems observed in this paper could be observed more frequently for automated
optical sensors. Smaller satellites are beginning to be used in GEO (e.g. Orbital’s Star-1 bus [11]) therefore colocation will not only be a problem of independently detecting two large, bright objects but smaller, fainter
objects. The success of the Orbital Express rendezvous mission [12] suggests that autonomous satellite
refuelling and repair may become a reality. This would create a new paradigm for optical deep space
Surveillance where monitoring of geostationary client satellites and manoeuvrable servicing satellites would
cause problems for tradition metric sensors as relative motion between client and servicer would come within 1
km of each other making the visual conjunction cases even more problematic. Traditional correlation techniques
using two line orbital elements would face significant challenges if these services became common.
10. REFERE&CES
1.
2.
Soup, E.M., "Handbook of Geostationary Orbits", ESA Space Technology Library, 1994.
Wauther, P., Francken P., Laroche, H.., “Co-location of Six Astra Satellites: Assessment after one year of
operations”, European Space Agency, 1997 ESASP.
3. B. Wallace, “The DRDC Ottawa Space Surveillance Observatory,” AMOS Technical Conference 2007,
September 2007.
4. Burlton ,B., et al., “Orbital Mechanics and Space Control, [Course Notes - MECH 5105],” Carleton
University, September 2007
5. M. Eckstein, C. Rajasignh, P. Blumer., “Co-location Strategy and Collision avoidance For the
Geostationary Satellites at 19 Degrees West,” Space Dynamics, C*ES, November 1989.
6. Vallado, D., "Fundamentals of Astrodynamics and Applications, Second Edition", Space Technology
Library, 2001.
7. Scott, L., Wallace, B., "Small Aperture Optical Photometry of Canadian Geostationary Satellites",
Canadian Aeronautics and Space Institute CASI, ASTRO 2008, Montreal, Quebec, April 2008.
8. Payne, T., et al. “SSA Analysis of GEOS Photometric Signature Classifications and Solar Panel Offsets”,
AMOS Technical Conference 2006, Maui HI.
9. Floherer, T., Krag., H., Klinkrad., H., “Assessment and Categorization of TLE Orbit Errors for the US
SSN Catalogue”, Proceedings of the 2008 AMOS Technical Conference 2008., Maui HI.
10. Africano, L., Kervin, P., Sydney P., Lambert., J., “Deep Space Optical Brightness Catalog”, AMOS
Technical Conference 2000.
11. Richmond, C., "The Growth of Orbital Sciences and the Market for Small GEO Satellites", SJR, No. 55
April/May 2008.
12. Mulder., “Orbital Express Autonomous Rendezvous and Capture Flight Operations – Part 1 or 2: Mission
Description, AR&C Exercises 1,2 and 3”, Spaceflight Mechanics Meeting 2008., Galveston TX. 2008.