Adaptive track estimation on a radar array system for space surveillance
Journal
Date Issued
2022
Author(s)
Montaruli, M. F.
•
Facchini, L.
•
Lizia, P. Di
•
Massari, M.
•
•
•
Abstract
The increasing population of resident space objects is currently fostering many Space Surveillance and Tracking (SST) initiatives, which are based on the use of ground sensors. Italy contributes to the European SST Support Framework with the BIstatic RAdar for LEo Survey (BIRALES). Orbit determination from BIRALES observations relies on the estimation of the angular profiles of the object crossing the sensor field of view by processing the receiver raw data. Based on the receiver array configuration, its field of view is currently populated with many independent beams. However, the results of the currently adopted orbit determination process are negatively affected by the simultaneous presence of multiple grating lobes in the receiver gain pattern and by the signal quality. Within this context, this work proposes a paradigm shift in BIRALES data processing by introducing a multiple signal classification (MUSIC) approach. First, in the track estimation phase, multiple signal directions of arrival are determined, at any time instant, from the data correlation matrix. The multiplicity of the solutions is due to the array geometry and yields ambiguity in track reconstruction. For the catalogued objects, this ambiguity can be solved by exploiting the availability of their orbital estimates. For the uncatalogued ones, it is solved by clustering the candidate signal directions of arrival and identifying the correct track using slant range, Doppler shift and signal to noise ratio measurements. Although introduced for BIRALES, the applicability of the method extends to any SST survey radar with an array receiver and with the ability to correlate the signals detected by each receiver element. Finally, algorithm performances and robustness are assessed on a large set of synthetic passes as well as on real measurements.
Volume
198
Start page
111
Issn Identifier
0094-5765
Ads BibCode
2022AcAau.198..111M
Rights
open.access
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