Introduction to Resident Space

What is/are Resident Space?

Resident Space - Numerical simulations demonstrate the advantage of using the Uncertainty Representation and Reasoning Evaluation Framework (URREF) for space ontological pattern of life assessment of veracity, precision, and recall when a resident space objects conducts a maneuver. ^[1] The simulator takes in a textured geometric model of a Resident Space Object as well as its ephemeris and uses ray-tracing software to generate photo-realistic images of the object that are then processed to extract the light curve. ^[2] This paper presents a novel approach to identify, localize, and associate resident space objects (i. ^[3] The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. ^[4] With easier access to space and mega-constellation plans, tracking the resident space object (RSO) population becomes a greater challenge. ^[5] Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. ^[6] The uncontrolled growing number of resident space objects (RSO) increases the likelihood of close conjunctions and therefore collisions that will populate the space environment even more. ^[7] With a growing number of resident space objects (RSOs), the facilities for near-Earth space surveillance have to cope with increasing workload. ^[8] Studying the operational frequency spectra, profile, and aural perception of an active satellite event such as a thruster ignition or any subsystem operation can provide unique signature identifiers that support resident space object characterization efforts. ^[9] 2 We commend the introduction of a dedicated resident space for such practice but would suggest that there are further ways in which this specifically designed task could be complemented by online tools. ^[10] The capability accurately and timely detect a resident space object (RSO) maneuver is a critical task for monitoring space activities. ^[11] The BIRALET system represents one of the main Italian radars involved in resident space objects observations. ^[12] In fact, it is vital to persistently observe and characterize resident space objects. ^[13] This paper defines a set of six non-singular orbital elements designed specifically for the characterization of uncertainty in the state of a resident space object in circular or elliptic orbit and demonstrates their use for uncertainty propagation in the context of the perturbed two-body problem of orbital mechanics. ^[14] Having to deal with short arcs, the data association’s problem becomes relevant: one must find more observations of the same resident space object to precisely determine its orbit. ^[15] Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. ^[16] The shape and attitude of resident space objects directly affect the orbit propagation via drag and solar radiation pressure. ^[17] If all resident space objects larger than 1 cm are considered, this number increases to an estimate of 700,000 objects. ^[18] The proposed approach is developed by taking into account the following facts: 1) only one monocular camera onboard the capturer satellite is available; 2) the a-priori knowledge of resident space objects (RSOs) is strictly limited; 3) the capture window estimation framework is independent from the capture modes. ^[19] A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). ^[20] An algorithm to guide the capture of a tumbling resident space object by a spacecraft equipped with a robotic manipulator is presented. ^[21] The population of resident space objects (RSOs) has increased drastically during the past years. ^[22] As resident space object populations grow and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space domain awareness using c. ^[23] Differential games are formulated and solved for an inspector satellite operating nearby a resident space object (RSO) in an elliptical orbit. ^[24] A parametric investigation of plasma wake geometry is conducted to determine the applicability of Coulomb actuation to resident space objects (RSOs) in low earth orbit (LEO). ^[25] The need of precise orbits of resident space objects to prevent collisions suggests the use of laser-ranging measurements for orbit determination and improvement. ^[26] In a simulation environment consisting of orbit propagation, measurement, estimation, and prediction processes, totally 12 resident space objects (RSOs) in solar-synchronous orbit (SSO), low Earth orbit (LEO), and medium Earth orbit (MEO) are simulated to compare the performance of three ML algorithms. ^[27] A machine learning (ML) approach has been recently proposed to improve the orbit prediction accuracy of resident space objects (RSOs) through learning from historical data. ^[28] There are many Resident Space Objects (RSOs) in the Geostationary Earth Orbit (GEO) regime, both operational and debris. ^[29] Effective space situational awareness/battlespace awareness (SSA/BA) requires determination of resident space object (RSO) characteristics. ^[30] The magnitude and complexity of its constituent lines of inquiry are growing at rapid pace, driven by the increasing number of objects of interest, including resident space objects: RSOs (a collective term for active spacecraft and space debris) as well as near Earth objects: NEOs (comets and asteroids in Earth’s vicinity). ^[31] The accurate prediction of the motion of resident space objects (RSOs) in Low Earth Orbit (LEO) requires knowledge of all forces acting on these objects, which includes active and inactive (e. ^[32] Building upon previous research, this paper presents a sensor-orientated approach to resident space object position. ^[33] They remain in the tracking of Resident Space Objects (RSO's) in Geosynchronous Orbits (GEO), due to limitations of SST and GEODSS system implementation. ^[34] AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). ^[35] In recent years, the number of resident space objects has increased dramatically. ^[36] The orbital safety of operational spacecraft, regardless of the mission, relies on timely and actionable observations to maintain so-called “custody” of all trackable Resident Space Objects (RSOs), including space debris, that might pose a hazard to safe, secure, and sustainable operations. ^[37] The GPO regime offers increased access to GEO resident space objects (RSO) since other orbits are restricted by a 40° solar exclusion angle. ^[38] The results are summarized for various initial conditions of the resident space object's attitude and angular velocity states. ^[39]

resident space object

Numerical simulations demonstrate the advantage of using the Uncertainty Representation and Reasoning Evaluation Framework (URREF) for space ontological pattern of life assessment of veracity, precision, and recall when a resident space objects conducts a maneuver. ^[1] The simulator takes in a textured geometric model of a Resident Space Object as well as its ephemeris and uses ray-tracing software to generate photo-realistic images of the object that are then processed to extract the light curve. ^[2] This paper presents a novel approach to identify, localize, and associate resident space objects (i. ^[3] The detection and identification of Resident Space Objects (RSOs) from survey tracks requires robust and efficient orbit determination methods for the association of observations of the same RSO. ^[4] With easier access to space and mega-constellation plans, tracking the resident space object (RSO) population becomes a greater challenge. ^[5] Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. ^[6] The uncontrolled growing number of resident space objects (RSO) increases the likelihood of close conjunctions and therefore collisions that will populate the space environment even more. ^[7] With a growing number of resident space objects (RSOs), the facilities for near-Earth space surveillance have to cope with increasing workload. ^[8] Studying the operational frequency spectra, profile, and aural perception of an active satellite event such as a thruster ignition or any subsystem operation can provide unique signature identifiers that support resident space object characterization efforts. ^[9] The capability accurately and timely detect a resident space object (RSO) maneuver is a critical task for monitoring space activities. ^[10] The BIRALET system represents one of the main Italian radars involved in resident space objects observations. ^[11] In fact, it is vital to persistently observe and characterize resident space objects. ^[12] This paper defines a set of six non-singular orbital elements designed specifically for the characterization of uncertainty in the state of a resident space object in circular or elliptic orbit and demonstrates their use for uncertainty propagation in the context of the perturbed two-body problem of orbital mechanics. ^[13] Having to deal with short arcs, the data association’s problem becomes relevant: one must find more observations of the same resident space object to precisely determine its orbit. ^[14] Using simulated ground-based electro-optical (EO) measurements for multiple resident space objects and multiple distributed EO sensors, the DeCiSpOT archived results comparable to an optimal centralized approach. ^[15] The shape and attitude of resident space objects directly affect the orbit propagation via drag and solar radiation pressure. ^[16] If all resident space objects larger than 1 cm are considered, this number increases to an estimate of 700,000 objects. ^[17] The proposed approach is developed by taking into account the following facts: 1) only one monocular camera onboard the capturer satellite is available; 2) the a-priori knowledge of resident space objects (RSOs) is strictly limited; 3) the capture window estimation framework is independent from the capture modes. ^[18] A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). ^[19] An algorithm to guide the capture of a tumbling resident space object by a spacecraft equipped with a robotic manipulator is presented. ^[20] The population of resident space objects (RSOs) has increased drastically during the past years. ^[21] As resident space object populations grow and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space domain awareness using c. ^[22] Differential games are formulated and solved for an inspector satellite operating nearby a resident space object (RSO) in an elliptical orbit. ^[23] A parametric investigation of plasma wake geometry is conducted to determine the applicability of Coulomb actuation to resident space objects (RSOs) in low earth orbit (LEO). ^[24] The need of precise orbits of resident space objects to prevent collisions suggests the use of laser-ranging measurements for orbit determination and improvement. ^[25] In a simulation environment consisting of orbit propagation, measurement, estimation, and prediction processes, totally 12 resident space objects (RSOs) in solar-synchronous orbit (SSO), low Earth orbit (LEO), and medium Earth orbit (MEO) are simulated to compare the performance of three ML algorithms. ^[26] A machine learning (ML) approach has been recently proposed to improve the orbit prediction accuracy of resident space objects (RSOs) through learning from historical data. ^[27] There are many Resident Space Objects (RSOs) in the Geostationary Earth Orbit (GEO) regime, both operational and debris. ^[28] Effective space situational awareness/battlespace awareness (SSA/BA) requires determination of resident space object (RSO) characteristics. ^[29] The magnitude and complexity of its constituent lines of inquiry are growing at rapid pace, driven by the increasing number of objects of interest, including resident space objects: RSOs (a collective term for active spacecraft and space debris) as well as near Earth objects: NEOs (comets and asteroids in Earth’s vicinity). ^[30] The accurate prediction of the motion of resident space objects (RSOs) in Low Earth Orbit (LEO) requires knowledge of all forces acting on these objects, which includes active and inactive (e. ^[31] Building upon previous research, this paper presents a sensor-orientated approach to resident space object position. ^[32] They remain in the tracking of Resident Space Objects (RSO's) in Geosynchronous Orbits (GEO), due to limitations of SST and GEODSS system implementation. ^[33] AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). ^[34] In recent years, the number of resident space objects has increased dramatically. ^[35] The orbital safety of operational spacecraft, regardless of the mission, relies on timely and actionable observations to maintain so-called “custody” of all trackable Resident Space Objects (RSOs), including space debris, that might pose a hazard to safe, secure, and sustainable operations. ^[36] The GPO regime offers increased access to GEO resident space objects (RSO) since other orbits are restricted by a 40° solar exclusion angle. ^[37] The results are summarized for various initial conditions of the resident space object's attitude and angular velocity states. ^[38]