Delta-k approach for space surveillance multireceiver radars

The increasing population of resident space objects is currently fostering many space surveillance initiatives. In this framework, on-ground multireceiver radars allow to reconstruct the target angular track, but the array configuration may cause the presence of multiple solutions and, if no pass prediction is available, the ambiguity cannot be solved a-priori. This work proposes an evolution of the Music Approach for Track Estimate and Refinement (MATER) algorithm. Given two different signals reflected by the same target, at each observation epoch their Direction Of Arrival (DOA) is estimated from the signal Covariance Matrix (CM) through the MUltiple SIgnal Classification (MUSIC) algorithm. Then, the possible ambiguous estimations are solved through the delta-k technique: the correct DOA is considered as the one featuring the smallest angular deviation comparing the two CM results. This process is repeated for all the epochs, and the DOAs are clustered according to the RANdom SAmple Consensus (RANSAC) algorithm. Finally, the most populated cluster is considered as the correct one, and the angular track is computed through a time regression of the two angular coordinates. The evolution of MATER algorithm is tested through numerical simulations. The algorithm converges to the correct solution in 100% of the cases, with an angular accuracy in the order of 1–10 mdeg.