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The in-orbit landscape, consisting of objects such as active satellites and space debris, is a complex network which requires careful monitoring and orchestration to ensure a safe space environment. Without thorough management, collision is a disruptive and costly risk which has the potential to interrupt mission critical services. Space Situational Awareness focuses on understanding the location of satellites and debris in orbit, as well as their physical profile, trajectory, and velocity<\/p>\n
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To provide the industry with a picture of the orbital landscape, data is gathered by optical sensors and radar systems which identify and track in-orbit movement of objects. At the Space Data Association, we also collate data from our participants which enhances our knowledge of the environment. This contribution of data is invaluable as it provides the system with further insight into the positioning of satellites, as well as planned flight manoeuvres. Once the raw data has been collected, it must be made usable by filtering, correlating, and ensuring it is fused to maintain orbital state vectors together with their associated uncertainties. Estimating uncertainty is a key component of Space Situational Awareness. This blog will explain the challenges surrounding the role of data, as well as its absence, in delivering an accurate SSA platform.<\/p>\n
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At present, Special Perturbation data provided by the US government, is the only comprehensive space object catalogue available for SSA and close approach screening purposes. However, this public catalogue of data does not provide information on satellite manoeuvres. Without information regarding manoeuvres, the effectiveness of Collision Avoidance (CA) data has its limitations. Due to these limitations, SDA members choose to share their orbit and manoeuvre data to enrich accuracy. Member data is screened by the SDA and supplemented with SP data and other third-party data available.<\/p>\n
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The next challenge in CA screening is that the preferred screening criteria is based on probability of collision (PoC), which accounts for satellite miss-distances as well as the uncertainties of data used. One of the difficulties is that SP data \u2013 as well as most O\/O (Owner\/Operator) ephemeris and third-party data \u2013 does not include realistic covariance. In SSA, covariance represents the accuracy of the satellite\u2019s orbit. In its absence, the generation of synthetic covariance utilising member data, SP data, and third-party data can help mitigate this issue.<\/p>\n
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Synthetic covariance of orbit is generated by comparing the variations of the ephemeris over time. By performing synthetic covariance, it provides an estimate of the orbit uncertainties consistent with the orbit ephemeris, improving the accuracy of CA.<\/p>\n
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Synthetic covariance also addresses when SP and third-party data does not contain information required and accounts for potential unknown\/unreported manoeuvres in the data. Together with orbit ephemeris and the synthetic covariance, this represents a consistent dataset that can be used for Collision Avoidance screening and provides realistic risks and information for effective decision making.<\/p>\n
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Utilising O\/O data is a key strength of the SDA\u2019s Space Safety Portal. However, its use must be done appropriately to ensure its effectivity. \u00a0<\/span>The following considerations must be addressed:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span><\/p>\n \n \n When performing screening using O\/O generated orbit ephemeris and covariance, it is important that the reference frame and time system of the data is well defined, and the shared orbit ephemeris and covariance are properly converted to the consistent frame among all operators for screening. Naming conventions can differ among operators, therefore great care must be taken when building a custom frame convertor for screening. To simplify and minimise the obstacles for operators to participate in SSA, SDA assumes the responsibilities on reference frame and time system conversion and allows the member to submit ephemeris in their native frame from their ground systems.<\/p>\n \n As the SDA performs synthetic covariance on all orbit ephemeris available for CA screening, this reduces the concerns regarding covariance not being in the same reference frame and time system.<\/p>\n \u00a0<\/strong><\/p>\n \n Most operators do not have a large network of tracking antennae to independently calibrate their tracking antennae measurement biases.\u00a0 <\/span>These errors will impact the accuracies of shared ephemeris and lead to incorrect CA results.\u00a0 <\/span>SDA regularly performs sanity screening using independent data sources to detect and identify O\/O ephemeris biases and inform our members.<\/p>\n \u00a0<\/strong><\/p>\n \n It is critical that O\/Os update their ephemeris regularly and in a timely manner to ensure the orbit is up to date, especially after manoeuvres have taken place. There must be a sufficient time span generated to provide sufficient screening intervals in the future.\u00a0 <\/span>Increased data latency reduces the effectiveness of the shared ephemeris. In these cases, SDA is working on a solution to reduce data latency and the workload on O\/Os by requesting them to regularly provide their observation data and manoeuvre information. The concept is that SDA will generate the orbit as well as the associated covariance based on the observation data and the O\/O manoeuvre plans. This solution is especially suitable for small operators as it takes away the burden of generating ephemeris on a regular basis and allows a continuous flow of observation data.\u00a0\u00a0\u00a0\u00a0 <\/span><\/p>\n \u00a0<\/strong><\/p>\n \n With consistent, thorough data screening and processing, O\/O data is a brilliant tool to enhance SSA. Paired with synthetic covariance, we see improved CA accuracy for users. Accuracy is key to Space Traffic Management and a holistic overview of screened and processed data, sourced from sensors, public catalogues, and O\/Os is key to delivering a world-leading SSA platform.<\/p>\n \n To learn more about the Space Data Association and its Space Safety Portal, reach out today.<\/p>\n [\/et_pb_text][et_pb_button button_text=”Space Safety Portal” _builder_version=”4.27.4″ _module_preset=”default” theme_builder_area=”post_content” url_new_window=”on” button_url=”https:\/\/www.space-data.org\/sda\/space-data-center\/” custom_button=”on” global_colors_info=”{%22gcid-083035b5-8a71-497e-afab-a4ebf1b61c1d%22:%91%22button_text_color%22%93}” button_text_color=”gcid-083035b5-8a71-497e-afab-a4ebf1b61c1d” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" The in-orbit landscape, consisting of objects such as active satellites and space debris, is a complex network which requires careful monitoring and orchestration to ensure a safe space environment. Without thorough management, collision is a disruptive and costly risk which has the potential to interrupt mission critical services. Space Situational Awareness focuses on understanding the location of satellites and debris in orbit, as well as their physical profile, trajectory, and velocity To provide the industry with a picture of the orbital landscape, data is gathered by optical sensors and radar systems which identify and track in-orbit movement of objects. At the Space Data Association, we also collate data from our participants which enhances our knowledge of the environment. This contribution of data is invaluable as it provides the system with further insight into the positioning of satellites, as well as planned flight manoeuvres. Once the raw data has been collected, it must be made usable by filtering, correlating, and ensuring it is fused to maintain orbital state vectors together with their associated uncertainties. Estimating uncertainty is a key component of Space Situational Awareness. This blog will explain the challenges surrounding the role of data, as well as its absence, in […]<\/p>\n","protected":false},"author":48,"featured_media":4294,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"footnotes":""},"categories":[36],"tags":[],"class_list":["post-4293","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"\nInaccuracies in reference frame and time system conversion<\/h2>\n
Unintended or identified measurement biases in O\/O orbit determination solutions<\/h2>\n
Consistent provision of sufficient data span and data latency for CA screening<\/h2>\n
Gaining a clear picture of the orbital landscape<\/h2>\n