Sensor management for enhanced catalogue maintenance of resident space objects

Abstract

The research detailed in this thesis explores the deployment of steerable sensors as an efficient means of improving the capability, capacity and timeliness of existing space surveillance systems, to provide superior levels of Space Situational Awareness (SSA) through enhanced sensor management. These improvements are necessary as the world's increasing reliance on spacefaring has brought about the accumulation of an enormous quantity of man-made objects orbiting the Earth. For almost 60 years the number of objects has grown, causing a commensurate increase in the likelihood of destructive collisions involving manned missions and important space assets. To predict and ideally prevent collisions, a number of agencies endeavour to track as many Resident Space Objects (RSOs) as possible. Recent events involving unprecedented surges in the number of RSOs have made it clear that the ability to continue to operate safely in Earth orbit will require enhancements to existing levels of SSA. The act of maintaining SSA is reliant on many sources of information, of which a primary source is the direct observation of RSOs by space surveillance sensors. These observations are utilised to compile and maintain a catalogue of RSO's orbital state estimates that is analysed to determine the likelihood of collision. Surveillance of this environment is a challenging task that currently has a large dependence on legacy systems and techniques that can benefit from modernisation via the introduction of contemporary technologies and methodologies. Due to potential benefits such as low cost, high accuracy, scalability, flexibility and automation, the large scale deployment of steerable sensors is proposed as a means of improving existing catalogue maintenance systems.Researching a judicious means of deploying and exploiting steerable sensors to improve the capability, capacity and timeliness of space surveillance networks requires consideration of the management of sensors at both a network and an individual level.The exploration begins at the network level with an analysis of existing practices for maintaining RSO catalogues to understand how catalogue accuracy is affected when steerable sensors are employed. Through numerical simulation, the effectiveness of the current state of the art in steerable sensors, a class of electro-optical sensor, is contrasted with traditional radar surveillance. The findings indicate that if the current state of the art in steerable sensors were to be widely deployed as the primary contributing sensors, catalogue accuracy would increase significantly. The findings also show that greater catalogue accuracy could be expected if effects caused by passive optical sensing to observability of RSO range and sensor availability can be minimised.Methods for improving observability and availability when using networks of optical sensors are investigated next. Measurement level sensor fusion and efficient analysis of the iii network's visibility of the RSO catalogue are considered. The investigation's result...

Diffusion-based cooperative space object tracking

“…2) between the vector from the Earth's center to the sensor and the vector from the Earth's center to the Sun should be greater than 102 deg. 19 With the sensor measurements and geometrical checks, a cooperative tracking method can be coordinated using distributed sensors. To demonstrate the advantages of the decentralized space object tracking approach, a cooperative centralized information filter approach is first presented.…”

“…When the association pair is determined, the information state contribution i k;j and the information matrix contribution I k;j of the j'th sensor can be obtained by Eqs. (19) and (20), respectively.…”

Cognitive Systems Engineering Applied to Decision Support in Space Situational Awareness

“…The sensor tasking problem is a high-dimensional, multiobjective, mixed-integer, nonlinear optimization problem, and current approaches focus on tractable subproblems (e.g., single objectives, limited target objects, limited sensors). Potential SSA sensor tasking needs include maintaining catalogs of space object state observations (DeMars, Hussein, Frueh, Jah, & Scott Erwin, 2015; Hobson, 2014), detecting maneuvers or other anomalies (Jaunzemis, Mathew, & Holzinger, 2016), and estimating control modes or behavior (Coder, Holzinger, & Linares, 2018; Hart et al, 2016; Holzinger, Wetterer, Luu, Sabol, & Hamada, 2014). These objectives are generally not complementary, especially given limited sensor resources, and the different objectives require different tasking approaches; for instance, characterization (e.g., anomaly detection) prefers many observations of a small subset of the catalog, whereas catalog maintenance prefers a diverse set of observations from as many objects as possible.…”

“…Covariance-minimizing sensor tasking approaches have been proposed primarily for catalog maintenance and collision avoidance tasks (Hobson, 2014; Linares & Furfaro, 2016). The covariance-based approach is effective in addressing decision-maker needs that can be directly mapped to state estimate uncertainty; however, not all decision-maker needs meet this condition.…”