Alejandro Pastor, GMV; Diego Escobar, GMV; Manuel Sanjurjo-Rivo, Universidad Carlos III de Madrid; Alberto Águeda, GMV
Keywords: Space Debris, Space Surveillance and Tracking, Catalogue Build-Up, Track Association, Orbit Determination
Abstract:
The number of resident space objects (RSO) is increasing year after year and therefore the sensing capabilities. Space Surveillance and Tracking (SST) systems are composed by sensors and on-ground processing infrastructure devoted to generating a catalogue of RSO: a robust automated database that contains information of every detected object. During surveillance, large areas of the sky are scanned to obtain data for both catalogue build-up and maintenance activities. The catalogue build-up process consists in detecting new objects to include them in the catalogue without any previous information, while maintenance entails the update of existing objects information. Hence, the catalogue build-up depends on the capability to detect new objects from measurements, packed as tracks, provided by a sensor network.
The catalogue represents one of the main outcomes of the SST activities and the provision of SST products (e.g. high-risk collisions, upcoming re-entries, fragmentations) is based on the information available on it. Therefore it is crucial to develop methods that enable the detection of new objects and the orbit estimation with enough accuracy to ensure future correlation (track-to-orbit correlation) when new tracks are received to update the already catalogued orbit. One of the most relevant features that make the track association problem so challenging is the existing coupling between detection and estimation, i.e. to identify a new object it is required to estimate its orbit, while only measurements belonging to the same object should be used in this estimation.
This work presents a novel sequential filtering algorithm able to identify new objects from both optical and radar measurements by associating uncorrelated tracks (UCTs) belonging to the same object. It uses several Initial Orbit Determination (IOD) and Orbit Determination (OD) methods in order to obtain a figure of merit to decide whether certain tracks belong to the same object or not. Instead of using a brute-force approach by evaluating all possible combinations of UCTs, several filters and complexity reduction techniques are applied to reduce the computation resources required. Furthermore, the association is performed on the measurements space (track-to-track correlation) rather than in the orbit space (track-to-orbit or orbit-to-orbit correlation). A generic object detection methodology based on track association is presented, with a special emphasis on the differences between radar tracks and optical tracklets. In the latter case, the challenge resides in the derivation of enough orbital information from a single tracklet (with less attributables than a radar track) to allow the application of filters and complexity reduction techniques. Hence, IOD methods for the optical tracklet association are far more complex and resource-consuming than those for the radar track association and classical approaches are not enough.
Results have shown that this strategy provides more reliable results than an association made on the orbit space, in terms of both false positives and number of missed objects. Several realistic simulated scenarios have been set up to evaluate the performance of this algorithm under a purely build-up scenario (cold start) and the results are presented. The performance of the methods is evaluated in terms of clear correlation metrics, such as true positives, false positives and false negatives. They prove that the proposed methods are able to provide excellent results for the track association problem, since most of the objects can be identified while providing a very low number of false detections. This is important during catalogue build-up, since the addition of wrong objects is very undesirable. The computational cost of the algorithm allows real-time processing of new tracks thanks to the selective generation and pruning that avoid evaluating all possible combinations.
Date of Conference: September 17-20, 2019
Track: Astrodynamics