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Trending analysis of historical conjunction data messages
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Trending analysis of historical conjunction data messages Daniel Moomey∗, Austin Potter, John C. Matchett1, Jennifer Thielke1 Space Safety Division, Air Force Safety Center, 9700G Ave, Kirtland AFB, NM 87117, United States
a b s t r a c t The United States Air Force maintains the Catalog of Earth orbiting satellites, or SATCAT. In February of 2009, a collision between Cosmos 2251 and Iridium 33 occurred. This event not only generated thousands of pieces of debris, but also heightened international interest in preventing future orbital collisions. Future potential collisions, or conjunctions, are predicted and warned of by the 18th Space Control Squadron (18 SPCS), across the space community, using Conjunction Data Messages (CDMs). The research team requested, processed, analyzed, and trended the historical catalog of 23 million CDMs across spatial and temporal domains for aggregate risk assessment in each of the major Earth orbit classes. This effort is in the spirit of the Air Force Safety Center’s (AFSEC’s) vision, which is to achieve a proactive safety culture of hazard identification and risk management across the Air Force, to prevent mishaps. The type and depth of analysis presented here will increase in the future to better inform the hazards of the orbital environment and cultivate space mishap prevention.
1. Background and motivation In addition to the AFSEC vision, the center’s mission is to safeguard Airmen, protect resources, and preserve combat capability. When first discussing how to cultivate and mature preventive safety within the Air Force space community, AFSEC’s Space Safety division (AFSEC/SES) was working to normalize operations with the other disciplines of flight, weapons, and occupational safety and one program in the flight division stood out to have hazards of a similar nature to orbital collisions. The Bird/wildlife Aviation Strike Hazard (BASH) program is a branch within AFSEC’s Flight Safety Division. It tracks and trends the frequency and severity of bird and other wildlife strikes at air bases around the world. The BASH program aggregates, correlates, and trends the data with the intent of developing and employing appropriate prevention tools for those region’s bird species. The intended effect for future flight ops is to reduce the hazards of bird and wildlife strikes from manifesting in a mishap. This information informs recommend changes to policy and environmental habitats for safer flight operations, as well as the employment of the Avian Hazard Advisory System. Over the last 20 years, the BASH program has had reported approximately 80 thousand wildlife strikes, resulting in approximately 500 million dollars in damage to Air Force flight resources. Without the BASH program, these numbers would certainly be higher. When the Iridium 33 (primary), and the non-operational Russian satellite, Cosmos 2251 (secondary), collided, it was the first hyperveloc-
ity satellite-to-satellite collision in history. This single event generated thousands of pieces of debris, and the 18 SPCS has not completely cataloged all of the debris objects, to this day for collision screening [1]. In the decade since this collision, the Combined Space Operations Center and 18 SPCS have modernized their approach to performing Conjunction Assessment (CA), expanded it to include all active satellites, and grown international relationships to share CA predictions. Today, much of the space surveillance data in the SATCAT is publically available through United States Strategic Command Space Situational Awareness sharing program at www.space-track.org. This sharing program informs others operating in space of potential collision events, to reduce the chances of future satellite collisions, preserving not only the satellite mission capability, but the resource of an accessible space environment as well. As BASH leverages avian strike and sighting reports to perform its advisory mission, the 18 SPCS leverages the SATCAT and its CA processes to perform a space version of BASH by transmitting CDMs, to warn of future orbital collision hazards to satellite owner/operators around the world. While 18 SPCS builds and maintains this repository of orbital data, their mission focuses on operational elements. As such, they have not taken on the task to analyze and trend the historical CDMs for space safety or other purposes. AFSEC/SES decided to take on the task of analyzing and trending CDMs to explore the data and trend environmental changes over space and time, as part of a larger effort to expand the discipline of space safety. It is important to clarify that the intent is to do far more in preven-
∗
Corresponding author. E-mail addresses: [email protected] (D. Moomey), [email protected] (A. Potter), [email protected] (J.C. Matchett), [email protected] (J. Thielke). 1 John Matchett and Jennifer Thielke were working for Dawson Corporation at the time, under contract to support the Space Safety Division of the Air Force Safety Center. https://doi.org/10.1016/j.jsse.2019.12.003 Received 18 October 2019; Received in revised form 18 December 2019; Accepted 29 December 2019 Available online xxx 2468-8967/© 2020 International Association for the Advancement of Space Safety. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: D. Moomey, A. Potter and J.C. Matchett et al., Trending analysis of historical conjunction data messages, Journal of Space Safety Engineering, https://doi.org/10.1016/j.jsse.2019.12.003
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Table 1 Example CDM fields used. MESSAGE_ID SATNO1_CONJ_SATNO2 SAT1 SATNO 5 DIGIT #
TCA MM/DD/YY HH:MM SAT1_NAME TEXT
MISS_DIST METERS SAT1_TYPE PL/RB/DEB
P_C COEFICIENT SAT1_RCS Mˆ2
VELOCITY_R M/S SAT1_MVR Y, N, N/A, UNK
VELOCITY_T M/S INDEX 1
VELOCITY_N M/S SAT2_NAME TEXT
CONJ_ALT METERS SAT2_TYPE PL/RB/DEB
SAT2_RCS Mˆ2
SAT2_MVR Y, N, N/A, UNK
3. Data
tive space safety in the future. Our goal is to include other data sources such as breakup events, CA maneuvers, space weather effects, and data mining of other information from existing sources, such as systems engineering databases. By correlating this information and identifying common systemic effects in time and space across similar orbital systems and subsystems, these data sets could help identify and mitigate hazards unique to space operations, beyond potential collisions. Growing these efforts will be necessary to develop mitigation strategies, changes to policy, lessons learned, and best practices. Efforts such as these are necessary to mature the discipline of space safety and prevent future mishaps.
In November 2018, AFSEC submitted an Orbital Data Request (ODR) to the 18 SPCS, requesting all CDMs since the Cosmos-Iridium collision. The 18 SPCS sent 23 million CDMs, representing all CDMs spanning 2014 through 2018. The data fields processed included a subset of those requested, which were also a subset of all available CDM fields. The fields of value used in this work shown in Table 1 include, a unique message ID field, the Time of Closest Approach (TCA), miss distance, collision probability, relative velocity components, the altitude of conjunction, and the following fields for both the primary and secondary objects: catalog number, object name, object type, average Radar Cross Section (RCS), and maneuverability flag. The team also added a field for a general-purpose index counter. [3] The data were parsed and re-aggregated to create files for each of the orbit classes, Low Earth Orbit (LEO), Middle Earth Orbit (MEO), Geosynchronous Orbit (GEO), and Highly Elliptical Orbit (HEO) with parameters for orbit class, apogee, perigee, orbital period (P), and orbital eccentricity (e), shown in Table 2. These orbital parameters were not directly present in the data. They were computed from the apogee and perigee values of the primary objects involved by importing the files to a MATLAB script and applying Kepler’s equations to solve for P, e, and semi-major axis (a), using Earth radius (rE ) of 6371 km and a gravitational parameter for the Earth (μE ) of 3.986∗ 1014 m3 /s−2 [4].
2. Limitations The limitations of this effort originate largely from the information available from the data and the tools available for the analysis. The information provided by the 18 SPCS in its CDMs is limited first in completeness and fidelity of the information in the SATCAT. Analytical predictions estimate that there are approximately 500 thousand objects larger than 1 cm2 orbiting the Earth, of which the 18 SPCS reliably maintains roughly 25 thousand [2]. This in-turn limits CA screening capabilities against small objects, not reliably observed or maintained as part of the SATCAT. The CDM standard published in 2013 provides several fields, which if well populated could yield useful information for the purposes of this study. However, some of the fields requested were sparse. These elements of low fidelity and instability limited the scope of the analysis in the historical timespan and the fields. The SATCAT set of data spanned from 2014 to late December 2018. Unfortunately, the data fields were not consistent until 2016, which with the analytical tools available, resulted in only three years of usable data for this study. This made it difficult to determine the best-fit functions when trending the data over time. As a result, we used only linear trending approximations. Satellite mass was not directly available from the data and an approximation was calculated, leveraging the Radar Cross Section (RCS) and existing literature to estimate mass for some objects, but this was not universally applied due to a breakdown of the accuracy of the assumption that RCS can be substituted for physical area, especially for objects above 10 m2 . The fields for maneuverability flag were not well populated. This limited the ability to normalize the risk to account for those systems, which have the ability to mitigate the risks of known conjunctions by maneuvering. The CDMs populated the field with the probability of collision in only 5% of the LEO cases, and did not yield sufficient data for trending. It is unclear why this is the case. Additionally, in some cases where the field was populated, the value was very low, beyond calculable precision. Thus, we were unable to extract the probability risk component directly. Miss distance was used instead as a proxy for the probability, along with conjunction rates calculated from summing the index of events for subsets of interest. The data also required significant reduction and parsing due to limitations in available computational resources. The team developed ad hoc parsing techniques throughout this effort to account for hardware limitation. Access to larger physical memory resources would remove this constraint and enable processing message fields in future work.
𝑟𝑃 = PERIGEE + 𝑟𝐸
(1)
𝑟𝐴 = APOGEE + 𝑟𝐸
(2)
𝑟𝑃 + 𝑟𝐴 =𝑎 2 √ 𝑎3 𝑃 = 2𝜋 𝜇E 𝑒=1−
𝑟 𝑟𝑃 = 𝐴 −1 𝑎 𝑎
(3) (4)
(5)
4. Methodology Upon approval of the ODR, the 18 SPCS sent the data electronically in 23 separate .csv files. These files went through data reduction, parsing, processing through several filters. AFSEC/SES performed calculations on the necessary fields and from that data set, created the trending plots shown in the results section below. The team also parameterized the data fields by either spatial and temporal domain, or whether they contributed to the probability or consequence of collision risk. A permutation matrix was developed to weight the value of each plot and determine the influence to the risk of a collision event. Fig. 1 shows the risk evaluation criteria. To process the data, each unique conjunction needed to be sampled evenly. Conjunctions are updated regularly, prior to the TCA. As a result, several CDMs are typically generated for a given conjunction. Therefore, reduction of the data was required to remove sample bias. To reduce the data, a python script was created to filter and extract data to include only a single CDM from each conjunction. A conjunction was determined to be unique by combining the TCA and the Primary Object Identifier. Of the 23 million CDMs, there were approximately 7 million 2
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Table 2 Orbit class parameters. CLASS
APOGEE
PERIGEE
P
e
LEO MEO GEO HEO
<6 K km >6 K km<35 K km >35 K km<36 K km 20 K km
N/A >6 K km<35 K km >35 K km<36 K km <6 K km
<225 min 225–1400 min 1400–1500 min N/A
≤.3 ≤.3 ≤.3 >0.3
Table 3 CDM parameter matrix. RISK
DOMAIN
SEVERITY
LIKELIHOOD
SPACE
TIME
VEL_MAG∗ PRI_OBJ_TYPE RCS COLL_KE∗
MISS_DIST COLL_PROBǂ MVR FLGǂ
ORBIT∗ ALTITUDE PIR_INC
TCA FREQ
∗ ǂ
Computed. Low Fidelity.
Fig. 1. Risk evaluation template.
of an abundance of caution for their assets and were requesting additional screenings, each with potentially different miss distance thresholds for reporting. Using the covariance estimates of the orbit estimations is another way to normalize the risk of these events to prevent misrepresenting the risk and to better filter the conjunctions of legitimate concern. However, the covariance fields were not included in the original ODR due to available physical memory to process the data. Thus the criteria of <1 km miss distance for LEO and < 5 km for other orbit classes were used. An assumption was also made that objects involved in conjunction events with close spacing and low relative velocity were aware of each other and were cooperative. This indicates a low risk for collision. These events were therefore excluded by adding the relative velocity filters of >100 m/s for LEO and >10 m/s for other orbit classes. The number of conjunction events below these thresholds in LEO were not significant. This filter was applied primarily to deal with oversampled populations such as the O3B constellation, where the operator requests additional screenings and their spacing and velocities are low because they are cooperatively operating in near proximity. I validated this with the 18 SPCS (thank you to Omitron and 18 SPCS for helping to identify the need for these additional considerations in filtering the messages). Once this dataset was established, the most relevant data fields were parameterized to include those which indicated the likelihood of a collision event, the severity of a collision event, and other information about the conjunction which allowed for trending across spatial (orbit class, altitude), temporal (TCA), and frequency (index count) domains. These parameters, shown in Table 3, were not always well populated in the data. Some needed to be calculated, and those identified in bold indicated the most influential factors to the risk placement. The original intent was to use the collision probability field as the primary likelihood component of the risk. Unfortunately, this data was only populated for approx. 5% of LEO conjunctions. Instead, miss distance was used as a proxy for the likelihood of an individual collision. Though well populated, it does not yield a direct way to calculate the likelihood of a collision. There was also interest in observing how often conjunctions could be mitigated utilizing the maneuverable field, but this data was also not well populated in the data set and was excluded from this effort. As a result, parsing out the instances where an owner/operator had the ability to mitigate the risk of collision by maneuvering was not accomplished. Other independent data sources regarding collision avoidance processes and capabilities are needed to analyze this aspect of mitigating orbital collision risks.
Fig. 2. Variation of miss distance in time.
unique conjunction events. The CDM closest to the TCA for each unique conjunction was selected to represent the conjunction, for trending purposes. The CDM data closest to TCA was selected because when looking at an individual set of CDMs relating to a single TCA, the miss distance generally trends toward a norm as TCA approaches. This effect is shown in Fig. 2 and was found to be common across many of the conjunctions analyzed, though magnitude and time span from the initial CDM to stabilization did vary and was sometimes stable throughout. It is hypothesized this effect could be explained by a delay from the identification of the conjunction to an increase in the tasking priority of the objects to higher observation rates and finally more accurate orbit determinations used for follow-up CA screenings. Unfortunately, the original data request did not include the necessary information about sensor tasking priority, nor the number of observations used for the differential corrections for the orbit estimations in each CDM to validate this and is left for future work. The data set was further refined to include only conjunctions of high concern. For LEO, this meant filtering the conjunctions to those with ≤1 km spacing and >100 m/s relative closing velocity. For MEO, GEO, HEO the conjunctions were filtered to ≤5 km spacing and >10 m/s relative closing velocity. This was necessary to prevent over-representing screening requests from satellite owner/operators who were acting out 3
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Table 4 Data plot permutation value matrix.
Fig. 3. Process flow summary.
Along with computing the primary object’s apogee and perigee values, there were a few other calculations which were performed on the data to evaluate the severity component of the risk matrix for each of the orbit classes. Collision energies, were computed to produce a relative basis of comparison for trending conjunction energies for the severity risk component. The inelastic equation for kinetic energy (KE) transfer from the secondary body to the primary body was used and is expressed in Joules (6). The magnitude of the velocity of impact (Vmag ) was computed from the vector components (Vr, Vt, Vn) using Eq. (7). Since satellite mass (m) was not an available field in the CDMs, RCS was used as a proxy for effective area (Aeff ), from which an estimate for a satellite’s mass was extracted, using Eq. (8) [5]. This yielded reasonable results for small objects, but was found to be non-linearly deviant from realistic mass values for larger objects such as high mass satellites and rocket bodies with an RCS >~10 m2 . To compensate for this, a threshold was used for satellite vehicles and rocket bodies of 3000 kg. This value represents a reasonable average for both inert second stage rocket bodies and satellites [6]. This skewed the KE values downward for rare high KE conjunctions, and they remained outliers. KE = (1∕2) m Vmag 2 Vmag| =
√
Vr 2 + Vt 2 + Vn2
( )1.86 m = 37.97 Aef f
of the color-coded plots from the permutation value matrix shown in Table 4 were evaluated for their value and influence on the risk profile of a given orbit regime. Those in green with the highlighted yellow border were found to be the primary drivers of the trending and risk placement. They include conjunction altitude v. KE, frequency v. conjunction altitude, and frequency v. TCA. Fig. 3 shows a summary of the method’s data flow. 5. Results 5.1. Low earth orbit (LEO) • The total number of conjunctions in LEO for the timespan was: 1373,259, with 222 K < 1 km spacing, and 134 K < 1 km spacing and >100 m/s. • 1627 Primary Objects, 1591 <1 km. • The percentage of conjunctions where two satellites were involved was: 37%. • The rate of conjunctions based on object number dropped off logarithmically, implying that relatively few objects account for a disproportionately high number of conjunctions.
(6)
Fig. 4 shows the total number of conjunctions per day, trended for the three-year timespan. The data indicates a linear increase over the last three years at a rate of 0.06 conjunctions/day, though this analysis suffers from a short time span. Fig. 5 shows the distribution of conjunction events in LEO. There is a high concentration below 850 km, and near 500 km. The figure is a function of the spatial density of satellites and other objects. In the future, it would be interesting to breakout a year-by-year trend analysis to see how these histograms may evolve from a perspective of prevalence and effect from atmospheric decay. It is speculated that the high concentration at 500 km may be a consequence of designing for upper limit compliance with the Orbital Debris Mitigation Standard Practices requirement to dispose of the spacecraft from orbit within 25 years of a
(7)
(8)
With these parameters defined, it was possible to develop risks plots in each orbit class, with trending on the risk components across spatial, temporal, and frequency domains. Though, the permutation matrix of plots grows by (n2 /2)-n/2. So, with 12 parameters to evaluate, there are 66 potential plots for each orbit class. Producing and evaluating each would be cumbersome. This is why the permutation matrix was developed. It subjectively qualified which plots would be included for risk determination. Each 4
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Fig. 7. Collision Risk matrix in LEO. Fig. 4. LEO conjunction rate over time.
5.2. Medium earth orbit (MEO) • The total number of conjunctions for a primary object in a MEO orbit is: 47,608 conjunctions. • Total # of individual conjunction events <5 km spacing 19,351; 305 <5 km spacing and >10 m/s. • 135 Individual Primary Objects; 68 >10 m/s. • MEO conjunctions were highly concentrated at altitudes of 8000 km and 19,000 km. • Nearly all of the conjunctions >10 m/s were from a single constellation. • Very low likelihood of collision event in MEO. • The percentage of conjunctions where two satellites are involved is: 78%. The number of conjunctions in MEO was shown to be over an order of magnitude less than in LEO. There was also a much higher rate of conjunctions involving two satellites. It was also interesting to see that the percentage of conjunctions involving a maneuverable satellite decreased. Lastly, the metric, which stuck out the most, which is also shown in Fig. 10, is that the single most frequently conjuncting MEO object accounts for nearly a third of all MEO conjunctions. Fig. 8 shows the number of conjunctions in MEO trended over time. The frequency increased non-linearly, and suddenly starting in August of 2018 with a total of 19,351 events which seemed to coincide with an owner/operator’s specific request for larger screening volumes, which emphasizes the necessity to apply the 5 km spacing filter to the data set. Fig. 9. When filtered for those events which have a closing velocity above 10 m/s, the plot becomes quantized to individual events per day and has remained steady with a total of 305 events. Fig. 10 shows that two prevalent altitudes dominate the conjunction rate. All other altitudes show negligible occurrence rate by comparison. The lower altitude features account for the majority of the data. They
Fig. 5. LEO conjunctions by 10 km bins.
Fig. 6. LEO Conjunction Kinetic Energy.
mission’s end. Orbit lifetimes in LEO are driven by atmospheric molecular density, an object’s surface area to mass ratio, and solar activity. A 25-year orbit life for many systems without propulsion tends to be 500–550 km [7]. Fig. 6 shows the distribution of collision energies in Gigajoules (GJ), across 10 km altitude bands. Note the spuriously high value at 400 km corresponds to conjunctions involving the ISS. Note, a decrease in KE values with altitude is expected as orbital velocity drops with altitude. What is important to note is the scale and altitudes, which deviate from the trend line. Fig. 7 shows the agregate risk placement for orbits below 850 km and above 850 km. Also, the arrows indicate the trending direction of the risk over the time span of the data.
Fig. 8. MEO conjunction rate over time. 5
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Fig. 9. MEO conjunction rate over time >10 m/s.
Fig. 12. MEO Collision Risk matrix.
Fig. 10. MEO conjunction 100 km bins. Fig. 13. GEO conjunction rate over time.
Fig. 11. MEO Conjunction Kinetic Energy. Fig. 14. GEO conjunctions by 10 km bins.
coincide with the altitudes of the GLONASS constellation and the O3B constellation. Fig. 11 shows the KE of conjunctions in MEO is increasing over time, and the data points have a wide variance while also being 2–5 times lower energy, on average. Fig. 12 shows the agregate risk placement for orbits at 8000 km and above 18,000 km. Also, the arrows indicate the trending direction of the risk over the time span of the data.
On initial glance, the results of the above metrics could seem somewhat surprising compared to other orbit regimes. Though, several GEO satellites orbit together in “clusters,” which may explain the high dropoff of the rate of conjunctions once distance and closing velocity filters are applied. This also explains the high rate of satellite-to-satellite interactions. Fig. 13 shows the number of GEO conjunctions per day over the fit span. The frequency has decreased over time. Given that the GEO population is not decreasing, it is encouraging to see a lower occurrence rate. Fig. 14 shows the occurrence rate of conjunctions by altitude bands. A normal distribution was observed over a mere 50 km with a drop in 2–3 orders of magnitude outside ±50 km of GEO altitude. This was expected, as the objects in geosynchronous orbit are largely in highly circular orbits. Fig. 15 shows a lull in collision energies at GEO altitude, indicating that objects have generally lower closing velocities, which is to be ex-
5.3. Geosynchronous earth orbit (GEO) • The total number of conjunctions for a primary object in a GEO orbit is: 1938,315; 24 K <5 km; 4 K <5 km and >10 m/s. • Primary Objects; 537; 480 <5 km. • Highly concentrated at GEO alt. • Conjunction rate slightly decreasing. • GEO conjunctions are low KE. • The percentage of conjunctions where two satellites are involved is: 99.8%. 6
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Fig. 15. GEO average KE by 50 km bins.
Fig. 18. HEO conjunction rate over time.
Fig. 16. GEO avg miss distance over time. Fig. 19. HEO conjunctions by 100 km bin.
Fig. 17. GEO collision risk matrix. Fig. 20. HEO avg KE by 100 km bins.
pected. The energies of collisions >10 m/s is three orders of magnitude lower at GEO altitude (where almost all the conjunctions occur) than in other orbit classes. Fig. 16 shows an increasing average miss distance, which, coupled with the decreasing occurrence rate is also encouraging. Fig. 17 shows a risk placement of low severity, but high likelihood of occurrence. The trend is slightly decreasing in likelihood due to increasing miss distances and reducing conjunction frequency. The risk placement was dominated by the circular geo-stationary population and the relatively low average impact energy.
• • • • •
Events concentrated at low altitude. Severity increasing over time. Inverse relationship with freq. & KE. Low likelihood, high impact. The percentage of conjunctions where two satellites are involved is: 35%. • The percentage of conjunctions where a satellite can maneuver is: 72.0%. • The most common object involved in a conjunction accounts for 20.5% of HEO conjunctions.
5.4. Highly eccentric orbit (HEO)
The satellite-to-satellite interaction rate was high, as was the rate at which maneuverable satellites were involved. This was the only regime where this was the case. Also, it appeared as though it had highly disproportionate interactions with the most frequent conjunction object, as in MEO.
• # of conjunctions: 52 K; 12 K <5 km. • 250 Primary Objects; 170 <5 km. • Likelihood increasing over time. 7
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is to grow and incorporate this effort as part of the new Orbital Hazard Identification Mitigation Program as one of the tools in the space safety tool bag. AFSEC/SES plans to continue to aggregate and analyze historical conjunction information for trending and other purposes, such as developing or changing policy, procedures, and best practices. To accomplish this, the current constraints of hardware and software processing resources which limited the scope of effort will need to be alleviated. It is expected that doing so will also open additional avenues to other data sources such as space weather, systems engineering databases, and owner/operator specific information. Aggregating, correlating, and analyzing this information will yield gains towards the goal of maturing the space safety discipline and preventing future space mishaps. Fig. 21. HEO collision risk matrix.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Fig. 18 shows a steady increase in the frequency of close conjunctions at a rate 100 conjunctions per year in HEO. Fig. 19 shows that the conjunction events are concentrated almost exclusively in LEO, when the primary satellite is at or near perigee and carrying the highest kinetic energy. Fig. 20 shows the kinetic energies to be extraordinarily high. The average energy levels also appear to be inversely proportional to the number of events at given altitudes. This may indicate a small sample bias at the low frequency altitudes. Fig. 21 shows the aggregate risk as high impact and low likelihood, primarily due to the low occurrence rate, though it is trending upwards for both perigee and apogee conjunctions.
References [1] USA Space Debris Environment, Operations, and Policy Updates. (2011, 02). UNOOSA., Retrieved 1 Feb, 2019 from http://www.unoosa.org/pdf/pres/stsc2011/ tech-31.pdf [2] Orbitaldebris.jsc.nasa.gov. (2019). ARES: Orbital Debris Program Office Frequently Asked Questions. [online] Available at: https://orbitaldebris.jsc.nasa.gov/ faq.html#top [Accessed 23 Jan, 2019]. [3] Conjunction Data Message (Blue Book) (pg. 1-72, Issue brief No. CCDS 508.0-B-1). (2013). Washington, DC. [4] J.R. Wertz, W.J. Larson, D. Kirkpatrick, D. Klungle, Table 6-2, pg. 137, pg. 897. Space Mission Analysis and Design, Space Technology Library published jointly by Microcosmrisk, Hawthorne, CA, 2010. [5] G.D. Badhwar, P.D. Anz-Meador, Determination of the area and mass distribution of orbital debris fragments, Earth Moon Planets 45 (1989) 29–51. [6] UCS Satellite Database. (2019, January 9). Retrieved April 2, 2019, from https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database [7] Wertz, J.R., Larson, W.J., Kirkpatrick, D., & Klungle, D. (2010), pg. 1032, Table I-1. Space Mission Analysis and Design. Hawthorne, CA
6. Conclusions and future work The largest takeaways from this work concerning risks of collisions in Earth orbit are that they are generally low. The high and medium risks are largely concentrated in LEO below 850 km, and circular GEO orbits. However, this analysis has raised several additional questions for research with a future ODR, to include additional data fields. Going forward, the intent is not to duplicate or repeat what the 18 SPCS does in informing space users of future conjunctions. The intent
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Journal of Space Safety Engineering journal homepage: www.elsevier.com/locate/jsse
Trending analysis of historical conjunction data messages Daniel Moomey∗, Austin Potter, John C. Matchett1, Jennifer Thielke1 Space Safety Division, Air Force Safety Center, 9700G Ave, Kirtland AFB, NM 87117, United States
a b s t r a c t The United States Air Force maintains the Catalog of Earth orbiting satellites, or SATCAT. In February of 2009, a collision between Cosmos 2251 and Iridium 33 occurred. This event not only generated thousands of pieces of debris, but also heightened international interest in preventing future orbital collisions. Future potential collisions, or conjunctions, are predicted and warned of by the 18th Space Control Squadron (18 SPCS), across the space community, using Conjunction Data Messages (CDMs). The research team requested, processed, analyzed, and trended the historical catalog of 23 million CDMs across spatial and temporal domains for aggregate risk assessment in each of the major Earth orbit classes. This effort is in the spirit of the Air Force Safety Center’s (AFSEC’s) vision, which is to achieve a proactive safety culture of hazard identification and risk management across the Air Force, to prevent mishaps. The type and depth of analysis presented here will increase in the future to better inform the hazards of the orbital environment and cultivate space mishap prevention.
1. Background and motivation In addition to the AFSEC vision, the center’s mission is to safeguard Airmen, protect resources, and preserve combat capability. When first discussing how to cultivate and mature preventive safety within the Air Force space community, AFSEC’s Space Safety division (AFSEC/SES) was working to normalize operations with the other disciplines of flight, weapons, and occupational safety and one program in the flight division stood out to have hazards of a similar nature to orbital collisions. The Bird/wildlife Aviation Strike Hazard (BASH) program is a branch within AFSEC’s Flight Safety Division. It tracks and trends the frequency and severity of bird and other wildlife strikes at air bases around the world. The BASH program aggregates, correlates, and trends the data with the intent of developing and employing appropriate prevention tools for those region’s bird species. The intended effect for future flight ops is to reduce the hazards of bird and wildlife strikes from manifesting in a mishap. This information informs recommend changes to policy and environmental habitats for safer flight operations, as well as the employment of the Avian Hazard Advisory System. Over the last 20 years, the BASH program has had reported approximately 80 thousand wildlife strikes, resulting in approximately 500 million dollars in damage to Air Force flight resources. Without the BASH program, these numbers would certainly be higher. When the Iridium 33 (primary), and the non-operational Russian satellite, Cosmos 2251 (secondary), collided, it was the first hyperveloc-
ity satellite-to-satellite collision in history. This single event generated thousands of pieces of debris, and the 18 SPCS has not completely cataloged all of the debris objects, to this day for collision screening [1]. In the decade since this collision, the Combined Space Operations Center and 18 SPCS have modernized their approach to performing Conjunction Assessment (CA), expanded it to include all active satellites, and grown international relationships to share CA predictions. Today, much of the space surveillance data in the SATCAT is publically available through United States Strategic Command Space Situational Awareness sharing program at www.space-track.org. This sharing program informs others operating in space of potential collision events, to reduce the chances of future satellite collisions, preserving not only the satellite mission capability, but the resource of an accessible space environment as well. As BASH leverages avian strike and sighting reports to perform its advisory mission, the 18 SPCS leverages the SATCAT and its CA processes to perform a space version of BASH by transmitting CDMs, to warn of future orbital collision hazards to satellite owner/operators around the world. While 18 SPCS builds and maintains this repository of orbital data, their mission focuses on operational elements. As such, they have not taken on the task to analyze and trend the historical CDMs for space safety or other purposes. AFSEC/SES decided to take on the task of analyzing and trending CDMs to explore the data and trend environmental changes over space and time, as part of a larger effort to expand the discipline of space safety. It is important to clarify that the intent is to do far more in preven-
∗
Corresponding author. E-mail addresses: [email protected] (D. Moomey), [email protected] (A. Potter), [email protected] (J.C. Matchett), [email protected] (J. Thielke). 1 John Matchett and Jennifer Thielke were working for Dawson Corporation at the time, under contract to support the Space Safety Division of the Air Force Safety Center. https://doi.org/10.1016/j.jsse.2019.12.003 Received 18 October 2019; Received in revised form 18 December 2019; Accepted 29 December 2019 Available online xxx 2468-8967/© 2020 International Association for the Advancement of Space Safety. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: D. Moomey, A. Potter and J.C. Matchett et al., Trending analysis of historical conjunction data messages, Journal of Space Safety Engineering, https://doi.org/10.1016/j.jsse.2019.12.003
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Table 1 Example CDM fields used. MESSAGE_ID SATNO1_CONJ_SATNO2 SAT1 SATNO 5 DIGIT #
TCA MM/DD/YY HH:MM SAT1_NAME TEXT
MISS_DIST METERS SAT1_TYPE PL/RB/DEB
P_C COEFICIENT SAT1_RCS Mˆ2
VELOCITY_R M/S SAT1_MVR Y, N, N/A, UNK
VELOCITY_T M/S INDEX 1
VELOCITY_N M/S SAT2_NAME TEXT
CONJ_ALT METERS SAT2_TYPE PL/RB/DEB
SAT2_RCS Mˆ2
SAT2_MVR Y, N, N/A, UNK
3. Data
tive space safety in the future. Our goal is to include other data sources such as breakup events, CA maneuvers, space weather effects, and data mining of other information from existing sources, such as systems engineering databases. By correlating this information and identifying common systemic effects in time and space across similar orbital systems and subsystems, these data sets could help identify and mitigate hazards unique to space operations, beyond potential collisions. Growing these efforts will be necessary to develop mitigation strategies, changes to policy, lessons learned, and best practices. Efforts such as these are necessary to mature the discipline of space safety and prevent future mishaps.
In November 2018, AFSEC submitted an Orbital Data Request (ODR) to the 18 SPCS, requesting all CDMs since the Cosmos-Iridium collision. The 18 SPCS sent 23 million CDMs, representing all CDMs spanning 2014 through 2018. The data fields processed included a subset of those requested, which were also a subset of all available CDM fields. The fields of value used in this work shown in Table 1 include, a unique message ID field, the Time of Closest Approach (TCA), miss distance, collision probability, relative velocity components, the altitude of conjunction, and the following fields for both the primary and secondary objects: catalog number, object name, object type, average Radar Cross Section (RCS), and maneuverability flag. The team also added a field for a general-purpose index counter. [3] The data were parsed and re-aggregated to create files for each of the orbit classes, Low Earth Orbit (LEO), Middle Earth Orbit (MEO), Geosynchronous Orbit (GEO), and Highly Elliptical Orbit (HEO) with parameters for orbit class, apogee, perigee, orbital period (P), and orbital eccentricity (e), shown in Table 2. These orbital parameters were not directly present in the data. They were computed from the apogee and perigee values of the primary objects involved by importing the files to a MATLAB script and applying Kepler’s equations to solve for P, e, and semi-major axis (a), using Earth radius (rE ) of 6371 km and a gravitational parameter for the Earth (μE ) of 3.986∗ 1014 m3 /s−2 [4].
2. Limitations The limitations of this effort originate largely from the information available from the data and the tools available for the analysis. The information provided by the 18 SPCS in its CDMs is limited first in completeness and fidelity of the information in the SATCAT. Analytical predictions estimate that there are approximately 500 thousand objects larger than 1 cm2 orbiting the Earth, of which the 18 SPCS reliably maintains roughly 25 thousand [2]. This in-turn limits CA screening capabilities against small objects, not reliably observed or maintained as part of the SATCAT. The CDM standard published in 2013 provides several fields, which if well populated could yield useful information for the purposes of this study. However, some of the fields requested were sparse. These elements of low fidelity and instability limited the scope of the analysis in the historical timespan and the fields. The SATCAT set of data spanned from 2014 to late December 2018. Unfortunately, the data fields were not consistent until 2016, which with the analytical tools available, resulted in only three years of usable data for this study. This made it difficult to determine the best-fit functions when trending the data over time. As a result, we used only linear trending approximations. Satellite mass was not directly available from the data and an approximation was calculated, leveraging the Radar Cross Section (RCS) and existing literature to estimate mass for some objects, but this was not universally applied due to a breakdown of the accuracy of the assumption that RCS can be substituted for physical area, especially for objects above 10 m2 . The fields for maneuverability flag were not well populated. This limited the ability to normalize the risk to account for those systems, which have the ability to mitigate the risks of known conjunctions by maneuvering. The CDMs populated the field with the probability of collision in only 5% of the LEO cases, and did not yield sufficient data for trending. It is unclear why this is the case. Additionally, in some cases where the field was populated, the value was very low, beyond calculable precision. Thus, we were unable to extract the probability risk component directly. Miss distance was used instead as a proxy for the probability, along with conjunction rates calculated from summing the index of events for subsets of interest. The data also required significant reduction and parsing due to limitations in available computational resources. The team developed ad hoc parsing techniques throughout this effort to account for hardware limitation. Access to larger physical memory resources would remove this constraint and enable processing message fields in future work.
𝑟𝑃 = PERIGEE + 𝑟𝐸
(1)
𝑟𝐴 = APOGEE + 𝑟𝐸
(2)
𝑟𝑃 + 𝑟𝐴 =𝑎 2 √ 𝑎3 𝑃 = 2𝜋 𝜇E 𝑒=1−
𝑟 𝑟𝑃 = 𝐴 −1 𝑎 𝑎
(3) (4)
(5)
4. Methodology Upon approval of the ODR, the 18 SPCS sent the data electronically in 23 separate .csv files. These files went through data reduction, parsing, processing through several filters. AFSEC/SES performed calculations on the necessary fields and from that data set, created the trending plots shown in the results section below. The team also parameterized the data fields by either spatial and temporal domain, or whether they contributed to the probability or consequence of collision risk. A permutation matrix was developed to weight the value of each plot and determine the influence to the risk of a collision event. Fig. 1 shows the risk evaluation criteria. To process the data, each unique conjunction needed to be sampled evenly. Conjunctions are updated regularly, prior to the TCA. As a result, several CDMs are typically generated for a given conjunction. Therefore, reduction of the data was required to remove sample bias. To reduce the data, a python script was created to filter and extract data to include only a single CDM from each conjunction. A conjunction was determined to be unique by combining the TCA and the Primary Object Identifier. Of the 23 million CDMs, there were approximately 7 million 2
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Table 2 Orbit class parameters. CLASS
APOGEE
PERIGEE
P
e
LEO MEO GEO HEO
<6 K km >6 K km<35 K km >35 K km<36 K km 20 K km
N/A >6 K km<35 K km >35 K km<36 K km <6 K km
<225 min 225–1400 min 1400–1500 min N/A
≤.3 ≤.3 ≤.3 >0.3
Table 3 CDM parameter matrix. RISK
DOMAIN
SEVERITY
LIKELIHOOD
SPACE
TIME
VEL_MAG∗ PRI_OBJ_TYPE RCS COLL_KE∗
MISS_DIST COLL_PROBǂ MVR FLGǂ
ORBIT∗ ALTITUDE PIR_INC
TCA FREQ
∗ ǂ
Computed. Low Fidelity.
Fig. 1. Risk evaluation template.
of an abundance of caution for their assets and were requesting additional screenings, each with potentially different miss distance thresholds for reporting. Using the covariance estimates of the orbit estimations is another way to normalize the risk of these events to prevent misrepresenting the risk and to better filter the conjunctions of legitimate concern. However, the covariance fields were not included in the original ODR due to available physical memory to process the data. Thus the criteria of <1 km miss distance for LEO and < 5 km for other orbit classes were used. An assumption was also made that objects involved in conjunction events with close spacing and low relative velocity were aware of each other and were cooperative. This indicates a low risk for collision. These events were therefore excluded by adding the relative velocity filters of >100 m/s for LEO and >10 m/s for other orbit classes. The number of conjunction events below these thresholds in LEO were not significant. This filter was applied primarily to deal with oversampled populations such as the O3B constellation, where the operator requests additional screenings and their spacing and velocities are low because they are cooperatively operating in near proximity. I validated this with the 18 SPCS (thank you to Omitron and 18 SPCS for helping to identify the need for these additional considerations in filtering the messages). Once this dataset was established, the most relevant data fields were parameterized to include those which indicated the likelihood of a collision event, the severity of a collision event, and other information about the conjunction which allowed for trending across spatial (orbit class, altitude), temporal (TCA), and frequency (index count) domains. These parameters, shown in Table 3, were not always well populated in the data. Some needed to be calculated, and those identified in bold indicated the most influential factors to the risk placement. The original intent was to use the collision probability field as the primary likelihood component of the risk. Unfortunately, this data was only populated for approx. 5% of LEO conjunctions. Instead, miss distance was used as a proxy for the likelihood of an individual collision. Though well populated, it does not yield a direct way to calculate the likelihood of a collision. There was also interest in observing how often conjunctions could be mitigated utilizing the maneuverable field, but this data was also not well populated in the data set and was excluded from this effort. As a result, parsing out the instances where an owner/operator had the ability to mitigate the risk of collision by maneuvering was not accomplished. Other independent data sources regarding collision avoidance processes and capabilities are needed to analyze this aspect of mitigating orbital collision risks.
Fig. 2. Variation of miss distance in time.
unique conjunction events. The CDM closest to the TCA for each unique conjunction was selected to represent the conjunction, for trending purposes. The CDM data closest to TCA was selected because when looking at an individual set of CDMs relating to a single TCA, the miss distance generally trends toward a norm as TCA approaches. This effect is shown in Fig. 2 and was found to be common across many of the conjunctions analyzed, though magnitude and time span from the initial CDM to stabilization did vary and was sometimes stable throughout. It is hypothesized this effect could be explained by a delay from the identification of the conjunction to an increase in the tasking priority of the objects to higher observation rates and finally more accurate orbit determinations used for follow-up CA screenings. Unfortunately, the original data request did not include the necessary information about sensor tasking priority, nor the number of observations used for the differential corrections for the orbit estimations in each CDM to validate this and is left for future work. The data set was further refined to include only conjunctions of high concern. For LEO, this meant filtering the conjunctions to those with ≤1 km spacing and >100 m/s relative closing velocity. For MEO, GEO, HEO the conjunctions were filtered to ≤5 km spacing and >10 m/s relative closing velocity. This was necessary to prevent over-representing screening requests from satellite owner/operators who were acting out 3
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Table 4 Data plot permutation value matrix.
Fig. 3. Process flow summary.
Along with computing the primary object’s apogee and perigee values, there were a few other calculations which were performed on the data to evaluate the severity component of the risk matrix for each of the orbit classes. Collision energies, were computed to produce a relative basis of comparison for trending conjunction energies for the severity risk component. The inelastic equation for kinetic energy (KE) transfer from the secondary body to the primary body was used and is expressed in Joules (6). The magnitude of the velocity of impact (Vmag ) was computed from the vector components (Vr, Vt, Vn) using Eq. (7). Since satellite mass (m) was not an available field in the CDMs, RCS was used as a proxy for effective area (Aeff ), from which an estimate for a satellite’s mass was extracted, using Eq. (8) [5]. This yielded reasonable results for small objects, but was found to be non-linearly deviant from realistic mass values for larger objects such as high mass satellites and rocket bodies with an RCS >~10 m2 . To compensate for this, a threshold was used for satellite vehicles and rocket bodies of 3000 kg. This value represents a reasonable average for both inert second stage rocket bodies and satellites [6]. This skewed the KE values downward for rare high KE conjunctions, and they remained outliers. KE = (1∕2) m Vmag 2 Vmag| =
√
Vr 2 + Vt 2 + Vn2
( )1.86 m = 37.97 Aef f
of the color-coded plots from the permutation value matrix shown in Table 4 were evaluated for their value and influence on the risk profile of a given orbit regime. Those in green with the highlighted yellow border were found to be the primary drivers of the trending and risk placement. They include conjunction altitude v. KE, frequency v. conjunction altitude, and frequency v. TCA. Fig. 3 shows a summary of the method’s data flow. 5. Results 5.1. Low earth orbit (LEO) • The total number of conjunctions in LEO for the timespan was: 1373,259, with 222 K < 1 km spacing, and 134 K < 1 km spacing and >100 m/s. • 1627 Primary Objects, 1591 <1 km. • The percentage of conjunctions where two satellites were involved was: 37%. • The rate of conjunctions based on object number dropped off logarithmically, implying that relatively few objects account for a disproportionately high number of conjunctions.
(6)
Fig. 4 shows the total number of conjunctions per day, trended for the three-year timespan. The data indicates a linear increase over the last three years at a rate of 0.06 conjunctions/day, though this analysis suffers from a short time span. Fig. 5 shows the distribution of conjunction events in LEO. There is a high concentration below 850 km, and near 500 km. The figure is a function of the spatial density of satellites and other objects. In the future, it would be interesting to breakout a year-by-year trend analysis to see how these histograms may evolve from a perspective of prevalence and effect from atmospheric decay. It is speculated that the high concentration at 500 km may be a consequence of designing for upper limit compliance with the Orbital Debris Mitigation Standard Practices requirement to dispose of the spacecraft from orbit within 25 years of a
(7)
(8)
With these parameters defined, it was possible to develop risks plots in each orbit class, with trending on the risk components across spatial, temporal, and frequency domains. Though, the permutation matrix of plots grows by (n2 /2)-n/2. So, with 12 parameters to evaluate, there are 66 potential plots for each orbit class. Producing and evaluating each would be cumbersome. This is why the permutation matrix was developed. It subjectively qualified which plots would be included for risk determination. Each 4
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Fig. 7. Collision Risk matrix in LEO. Fig. 4. LEO conjunction rate over time.
5.2. Medium earth orbit (MEO) • The total number of conjunctions for a primary object in a MEO orbit is: 47,608 conjunctions. • Total # of individual conjunction events <5 km spacing 19,351; 305 <5 km spacing and >10 m/s. • 135 Individual Primary Objects; 68 >10 m/s. • MEO conjunctions were highly concentrated at altitudes of 8000 km and 19,000 km. • Nearly all of the conjunctions >10 m/s were from a single constellation. • Very low likelihood of collision event in MEO. • The percentage of conjunctions where two satellites are involved is: 78%. The number of conjunctions in MEO was shown to be over an order of magnitude less than in LEO. There was also a much higher rate of conjunctions involving two satellites. It was also interesting to see that the percentage of conjunctions involving a maneuverable satellite decreased. Lastly, the metric, which stuck out the most, which is also shown in Fig. 10, is that the single most frequently conjuncting MEO object accounts for nearly a third of all MEO conjunctions. Fig. 8 shows the number of conjunctions in MEO trended over time. The frequency increased non-linearly, and suddenly starting in August of 2018 with a total of 19,351 events which seemed to coincide with an owner/operator’s specific request for larger screening volumes, which emphasizes the necessity to apply the 5 km spacing filter to the data set. Fig. 9. When filtered for those events which have a closing velocity above 10 m/s, the plot becomes quantized to individual events per day and has remained steady with a total of 305 events. Fig. 10 shows that two prevalent altitudes dominate the conjunction rate. All other altitudes show negligible occurrence rate by comparison. The lower altitude features account for the majority of the data. They
Fig. 5. LEO conjunctions by 10 km bins.
Fig. 6. LEO Conjunction Kinetic Energy.
mission’s end. Orbit lifetimes in LEO are driven by atmospheric molecular density, an object’s surface area to mass ratio, and solar activity. A 25-year orbit life for many systems without propulsion tends to be 500–550 km [7]. Fig. 6 shows the distribution of collision energies in Gigajoules (GJ), across 10 km altitude bands. Note the spuriously high value at 400 km corresponds to conjunctions involving the ISS. Note, a decrease in KE values with altitude is expected as orbital velocity drops with altitude. What is important to note is the scale and altitudes, which deviate from the trend line. Fig. 7 shows the agregate risk placement for orbits below 850 km and above 850 km. Also, the arrows indicate the trending direction of the risk over the time span of the data.
Fig. 8. MEO conjunction rate over time. 5
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Fig. 9. MEO conjunction rate over time >10 m/s.
Fig. 12. MEO Collision Risk matrix.
Fig. 10. MEO conjunction 100 km bins. Fig. 13. GEO conjunction rate over time.
Fig. 11. MEO Conjunction Kinetic Energy. Fig. 14. GEO conjunctions by 10 km bins.
coincide with the altitudes of the GLONASS constellation and the O3B constellation. Fig. 11 shows the KE of conjunctions in MEO is increasing over time, and the data points have a wide variance while also being 2–5 times lower energy, on average. Fig. 12 shows the agregate risk placement for orbits at 8000 km and above 18,000 km. Also, the arrows indicate the trending direction of the risk over the time span of the data.
On initial glance, the results of the above metrics could seem somewhat surprising compared to other orbit regimes. Though, several GEO satellites orbit together in “clusters,” which may explain the high dropoff of the rate of conjunctions once distance and closing velocity filters are applied. This also explains the high rate of satellite-to-satellite interactions. Fig. 13 shows the number of GEO conjunctions per day over the fit span. The frequency has decreased over time. Given that the GEO population is not decreasing, it is encouraging to see a lower occurrence rate. Fig. 14 shows the occurrence rate of conjunctions by altitude bands. A normal distribution was observed over a mere 50 km with a drop in 2–3 orders of magnitude outside ±50 km of GEO altitude. This was expected, as the objects in geosynchronous orbit are largely in highly circular orbits. Fig. 15 shows a lull in collision energies at GEO altitude, indicating that objects have generally lower closing velocities, which is to be ex-
5.3. Geosynchronous earth orbit (GEO) • The total number of conjunctions for a primary object in a GEO orbit is: 1938,315; 24 K <5 km; 4 K <5 km and >10 m/s. • Primary Objects; 537; 480 <5 km. • Highly concentrated at GEO alt. • Conjunction rate slightly decreasing. • GEO conjunctions are low KE. • The percentage of conjunctions where two satellites are involved is: 99.8%. 6
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Fig. 15. GEO average KE by 50 km bins.
Fig. 18. HEO conjunction rate over time.
Fig. 16. GEO avg miss distance over time. Fig. 19. HEO conjunctions by 100 km bin.
Fig. 17. GEO collision risk matrix. Fig. 20. HEO avg KE by 100 km bins.
pected. The energies of collisions >10 m/s is three orders of magnitude lower at GEO altitude (where almost all the conjunctions occur) than in other orbit classes. Fig. 16 shows an increasing average miss distance, which, coupled with the decreasing occurrence rate is also encouraging. Fig. 17 shows a risk placement of low severity, but high likelihood of occurrence. The trend is slightly decreasing in likelihood due to increasing miss distances and reducing conjunction frequency. The risk placement was dominated by the circular geo-stationary population and the relatively low average impact energy.
• • • • •
Events concentrated at low altitude. Severity increasing over time. Inverse relationship with freq. & KE. Low likelihood, high impact. The percentage of conjunctions where two satellites are involved is: 35%. • The percentage of conjunctions where a satellite can maneuver is: 72.0%. • The most common object involved in a conjunction accounts for 20.5% of HEO conjunctions.
5.4. Highly eccentric orbit (HEO)
The satellite-to-satellite interaction rate was high, as was the rate at which maneuverable satellites were involved. This was the only regime where this was the case. Also, it appeared as though it had highly disproportionate interactions with the most frequent conjunction object, as in MEO.
• # of conjunctions: 52 K; 12 K <5 km. • 250 Primary Objects; 170 <5 km. • Likelihood increasing over time. 7
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is to grow and incorporate this effort as part of the new Orbital Hazard Identification Mitigation Program as one of the tools in the space safety tool bag. AFSEC/SES plans to continue to aggregate and analyze historical conjunction information for trending and other purposes, such as developing or changing policy, procedures, and best practices. To accomplish this, the current constraints of hardware and software processing resources which limited the scope of effort will need to be alleviated. It is expected that doing so will also open additional avenues to other data sources such as space weather, systems engineering databases, and owner/operator specific information. Aggregating, correlating, and analyzing this information will yield gains towards the goal of maturing the space safety discipline and preventing future space mishaps. Fig. 21. HEO collision risk matrix.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Fig. 18 shows a steady increase in the frequency of close conjunctions at a rate 100 conjunctions per year in HEO. Fig. 19 shows that the conjunction events are concentrated almost exclusively in LEO, when the primary satellite is at or near perigee and carrying the highest kinetic energy. Fig. 20 shows the kinetic energies to be extraordinarily high. The average energy levels also appear to be inversely proportional to the number of events at given altitudes. This may indicate a small sample bias at the low frequency altitudes. Fig. 21 shows the aggregate risk as high impact and low likelihood, primarily due to the low occurrence rate, though it is trending upwards for both perigee and apogee conjunctions.
References [1] USA Space Debris Environment, Operations, and Policy Updates. (2011, 02). UNOOSA., Retrieved 1 Feb, 2019 from http://www.unoosa.org/pdf/pres/stsc2011/ tech-31.pdf [2] Orbitaldebris.jsc.nasa.gov. (2019). ARES: Orbital Debris Program Office Frequently Asked Questions. [online] Available at: https://orbitaldebris.jsc.nasa.gov/ faq.html#top [Accessed 23 Jan, 2019]. [3] Conjunction Data Message (Blue Book) (pg. 1-72, Issue brief No. CCDS 508.0-B-1). (2013). Washington, DC. [4] J.R. Wertz, W.J. Larson, D. Kirkpatrick, D. Klungle, Table 6-2, pg. 137, pg. 897. Space Mission Analysis and Design, Space Technology Library published jointly by Microcosmrisk, Hawthorne, CA, 2010. [5] G.D. Badhwar, P.D. Anz-Meador, Determination of the area and mass distribution of orbital debris fragments, Earth Moon Planets 45 (1989) 29–51. [6] UCS Satellite Database. (2019, January 9). Retrieved April 2, 2019, from https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database [7] Wertz, J.R., Larson, W.J., Kirkpatrick, D., & Klungle, D. (2010), pg. 1032, Table I-1. Space Mission Analysis and Design. Hawthorne, CA
6. Conclusions and future work The largest takeaways from this work concerning risks of collisions in Earth orbit are that they are generally low. The high and medium risks are largely concentrated in LEO below 850 km, and circular GEO orbits. However, this analysis has raised several additional questions for research with a future ODR, to include additional data fields. Going forward, the intent is not to duplicate or repeat what the 18 SPCS does in informing space users of future conjunctions. The intent
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