This page provides a description of the DIT module scoring methodology. This module has been co-developed by the University of Texas at Austin and the Space Enabled research group at MIT. Further details and justification of the scoring standards cut-offs, ground sensor capabilities and specifications can be found in HERE.
|Anthropogenic Space Object
|Computer Aided Design
|Detectability, Identification, and Trackability
|Ground Sensor Network
|Right Ascension of the Ascending Node
|Radar Cross Section
|Resident Space Object
|Space Situational Awareness
|Space Sustainability Rating
|System Tool Kit
The physical attributes of a satellite and the concept of operations affect the ability of sensors located on Earth to detecting, identifying, and tracking it. The goals of the Detectability, Identification, and Trackability (DIT) module are to encourage satellite operators to consider how the physical attributes of their satellite design and their operational approach during launch, operations and disposal affect the level of difficulty for observers to detect, identify, and track the satellite. By providing a consistent method to analyse a given satellite design and operational concept, this module will provide a standard metric for the comparison of satellite missions in the dimensions of detection, identification and tracking. Also, there is potential for this part of the SSR to encourage development of new ways for satellites operators to balance the considerations of how to limit their contributions to astronomic light pollution while maintaining their ability to be detected, identified, and tracked when necessary. In all, similar to the rest of the DIT module analyses aim to start conversations about how operators can utilize space more sustainably and responsibly.
Key definitions that drive the analysis include:
This definition considers the scenario in which a space surveillance system using optical and radar sensors to observe Anthropogenic Space Objects (ASOs) is monitoring for spacecrafts without having a specific list of objects and without a priori knowledge of their size, altitude or orbital characteristics. For this uninformed case, the Detection analysis asks the likelihood that a spacecraft in a given orbit can be detected separately by optical telescopes and surveillance radars. The Detectability of a set of mission spacecraft is therefore defined as the likelihood that the optical telescope and surveillance radar system will observe an ASO, subject to sources of error from the sensors, from signal loss as it propagates through the atmosphere and from illumination constraints due to the geometry of the sun, spacecraft and sensor.
Identification refers to the process in which an observer who does not have a priori information about the name, ownership, range and size of spacecraft, uses information gained through physical observations to gradually specify how difficult it is for an uninformed observer to uniquely distinguish a given spacecraft from others using only measurable or inferred characteristics independent of coordination with a spacecraft operator to complete the identification process. In practice, it is very challenging to distinguish a spacecraft from others, particularly considering increasing concerns in the future about the challenge of distinguishing satellites via ground-based data, especially with an increase in small satellites and constellations. Thus, the analysis shows the difficulty by calculating the angular momentum for a spacecraft and identifying a group of satellites that are found to be in a mathematical cluster based on angular momentum. The smaller the cluster, the easier it will be to identify the spacecraft.
Tracking refers to the process in which an observer has already detected and identified a spacecraft and next seeks to monitor and predict the evolution of the orbit of the spacecraft over time. The Tracking analysis asks how difficult it is for an observer who is not the satellite operator to perform the tracking function. In this case, the assumption is that the satellite tracker has information about the name, owner and instantaneous location of a satellite at a specific time. However, the observer does not have full knowledge of the orbital parameters. In this situation, the uncertainty of the tracking information increases when the access times are shorter for a ground station to observe a spacecraft. Thus, the trackability analysis computes access times as a figure of merit to estimate the level of uncertainty in the tracking process. More frequent overpasses of a ground-based network of telescopes and radars improve the prediction for when the spacecraft will pass within the field of regard again.
This following section presents the theoretical definitions that drives the Detectability, Identification and Trackability module and presents the methods to implement the first version of the software code using the tools Systems Tool Kit (from AGI) and MATLAB (from MathWorks).
Figure 1: Overview of Analysis Procedure to Calculate Detectability, Identification and Tracking
The analysis introduced above seeks to quantify the DIT of a given RSO independently of the capabilities of its operator to track the satellite and to reduce the error in estimating the satellite’s location. Thus, the analysis considers the perspective of an independent observer that is only working with information available through sensor observations of Anthropogenic Space Objects. Considerations of the level of error for the operator’s estimates of satellite location will be considered in the Collision Avoidance section of the SSR. Using primarily simulation, the DIT analyses will start with a set of physical assumptions and initial data requested from the operator. The data requested from the satellite operator partly overlap with the data requested for the Mission Index module which is used in the calculation of the Space Traffic Footprint.
The list of parameters includes:
- Geometric Approximation and Dimensions (rectangular prism, cylinder, or sphere)
- (Optional) Simplified CAD Model – Basic size and geometry
- (Optional) Detailed CAD Model - Complex faceted model (a single diffused facet can be used to average surface irregularities in order to provide an appropriate representation of material surfaces e.g. MLI wrinkling)
- Operational Orbit Parameters (apogee altitude, perigee altitude, inclination, RAAN, argument of perigee, mean anomaly)
A key component of the DIT analysis described here is the Ground Sensor Network (GSN) Capabilities assumed for the model. The ideal GSN for the SSR is one that represents capabilities attainable through commercially procured sensors that are available to countries around the world. The reason for this is that the SSR is intended to use transparent metrics to the extent possible. To accomplish this, the GSN modelled for the SSR is not representative of any specific existing GSN. In particular, the GSN used for these analyses is made up of sensors with capabilities on par with commercially available telescopes and radar systems. The ground sensors are distributed geographically within the simulation in order to give similar coverage to a variety of orbits. More details on the GSN can be found HERE.
The detectability analysis created for the first iteration of the SSR has two components: optical detection and radar detection. These represent the two most predominant methods for gathering data on satellites and other ASOs. These two portions of the analysis are represented in the top two rows of the analysis flowchart above. The goal of each of these analyses is to estimate how difficult it will be to detect a proposed ASO using each method individually and then translate that difficulty into a scorable metric for the SSR. The overall Detectability score is a combination of the Optical contribution and the Radar contribution.
Thus, Detectability is evaluated as:
Detectability Score = 0.5 x Optical Score + 0.5 x Radar Score
For this first iteration of the SSR, the optical detectability testing employs a binary scoring method with one threshold between detectability. This threshold is set at a visual magnitude of 15, which represents the limiting magnitude of an optically idealized 0.25m telescope. In this context, optically idealized means that the telescope itself does not introduce any error into the optical detection process. In practice, imperfections in the lenses, mirrors, and electronics of optical sensors lower the limiting magnitude of the overall optical system. This means that the scoring cut-off of 15th visual magnitude between “Detectable” and “Not Detectable” corresponds to an idealized 0.25m telescope as well as a non-idealized 0.3m-0.5m telescope. Telescopes of this class were selected for the lowest cut-off based in-part on the work done at the Air Force Research Lab on “Raven automated small telescope systems”. This study explored and validated the concept of using commercially available telescopes of size less than < 0.5m for satellite observation and tracking. If an ASO meets the 15th magnitude cut-off, it receives a score of 1, and if it does not meet the cut-off it receives a score of 0.5 for the Optical portion of the Detectability Score.
In radar analysis, a detection event occurs when the returning radar signal from the detected object is strong enough to be distinguished from the background noise with a certain level of confidence. For the DIT Radar Detection analysis, there are three cut-offs set to delineate between ASOs that are minimally detectable, ones that should be easier to detect, and ones that should be nearly guaranteed to be detected. A detection event with a probability of detection over 50% is considered a successful detection[*].
In order to differentiate ASOs that barely make the minimal detectability cut-off from those that handily exceed it, the Radar Detectability employs one additional cut-off at 75%. If an ASO meets the 50% threshold it receives a Radar score of 0.5, if it meets the 75% threshold it receives a Radar score of 1, otherwise it receives a Radar score of 0.
Identification refers to the process in which a naïve observer, who does not have a priori information about the name, ownership, range and size of spacecrafts, uses information gained through physical observations from earth to gradually specify that
- a specific object observed from ground-based sensors is observed repeatedly; and
- that it can be identified as an object with a specific name, orbit and owner.
This analysis asks how difficult it is for an uninformed observer to uniquely distinguish a given spacecraft from others using only observable characteristics independent of coordination with a spacecraft operator to complete the identification process. This analysis is not included in the DIT scoring at this time.
In the final portion of the analysis, the SSR assesses the trackability of a satellite by analysing the level of certainty with which an independent observer can estimate the future evolution of the orbit of a spacecraft. This stage assumes that an observer uses the reference optical observation system and a tracking radar system that is tuned to the appropriate parameters to observe an object of the given size and range (as identified in the Detectability analysis). The analysis calculates the future predicted periods in which the Detected space object will overfly the telescopes and radars in the ground sensor network. The analysis calculates the level of uncertainty for the estimates of future overpasses. The higher the uncertainty in the future orbital trajectory estimates, the lower the trackability score.
In order to provide an empirical basis for selecting scoring cut-offs, TLEs of approximatively 3200 active satellites were extracted from Celestrak. These satellites were then run through the trackability analysis to produce distributions to help identify trends in the pass duration, orbital coverage, and interval duration metrics. The cut-offs described below were defined through a combination of information from literature on the topic and from observations of the empirical data produced for ~3200 active missions.
Scoring for the Trackability analysis is broken down into three components with two sub scores for each. The first component is based on the ASO’s average pass duration, with scoring cut-offs at 120s (.25), 180s (.5 pts), and 400s (1 pts). The second component is based on the ASO’s average orbital coverage, with scoring cut-offs at 10% (.25 pts), 25% (.5 pts), and 60% (1 pt). The third component is based on the ASO’s average interval duration, with scoring cut-offs at 12hrs (.5 pts) and 6hrs (1 pt).
The total score for trackability is calculated as follows:
Trackability = 1⁄3 Pass Duration + 1⁄3 Average Orbital Coverage + 1⁄3 Interval Duration
The best achieved score between optical and radar trackability is used as the Trackability score in order to reflect that after a successful detection, one trackability method would be used over the other based on performance.
Questionnaire Portions for DIT
The final component of the DIT analysis is an additional qualitative score derived from an operator’s responses to the questionnaire. Certain aspects of the DIT processes cannot be quantitatively assessed at this time, so the questionnaire includes a few questions that evaluate the performance of the operator in the areas of satellite characterization and tracking, including:
Do you track the resident space objects you operate?
- Operator depends on Space-track, other third party public SSA providers, or is tracking the RSO by its own means. (1 points)
- Operator or contracted SSA Service Provider identifies and maintains custody of operated satellites within 14 days of deployment and thereafter. (2 point)
- Operator or contracted SSA Service Provider identifies and maintains custody of operated satellite within (1) day of deployment and thereafter. (3 points)
Provide verifiable photometric/radiometric characterisation data on the satellite to the SSR evaluator.
- Radiometric Data (average/max/min RCS) (2 point)
- Photometric Data (average/max/min Visual Magnitude) (2 point)
The final DIT Score is calculated as follows:
DIT Score = 1⁄3 Detectability Score + 1⁄3 Trackability Score + 1⁄3 Qualitative Score