Adaptive track approach for multiple sources scenarios during radar survey for space surveillance applications

The increasing population of resident space objects is currently fostering many space surveillance initiatives, which rely on the use of on-ground sensors. In particular, survey radars allow to first characterise the target orbit from a single transit, through measurements which are Doppler shift, slant range and angular profile. In this framework, the Music Approach for Track Estimate and Refinement (MATER) algorithm was developed to compute the angular track through the MUltiple SIgnal Classification (MUSIC), by solving possible ambiguities which may arise because of the receiver array geometry. This work presents the MATER extension to the case in which multiple sources are simultaneously detected by the sensor. For each detected source, the signal Direction of Arrival (DOA) is computed through MUSIC, and, if no prediction is exploited, possible ambiguous solutions arise. The computation is repeated for the entire observation, and all the estimations related to a specific target are grouped based on the angular sequence shape and the detection epochs. Finally, the possible ambiguity problem is solved, and the angular profile is obtained through a quadratic regression in time. The algorithm is numerically tested on a survey observation simulation. The detection length depends on the impinging signal intensity, and the angular accuracy is in the order of 1e-03 deg. A sensitivity analysis highlights that a transmitted power decrease shortens the detection length, with no remarkable angular accuracy deterioration. An additional simulation shows that in proximity operation monitoring MATER performance depends on the mutual geometry between target and chaser, as it may bring down the reciprocal angular distance under the resolution level. Nevertheless, it is always possible to identify the presence of both sources through the eigenvalues analysis of the signal correlation matrix. Finally, the simulation of a fragments cloud observation highlights that MATER performance depends both on the size of the observed fragment, as this is strictly linked to the signal detected by the receiver, and on the simultaneous detection of other fragments with a more effective signal.