Joint Calibration and Direct Position Determination for Moving Array Sensors

This paper addresses the problem of joint calibration and direct position determination (DPD) for moving array sensors. DPD techniques often rely on high-resolution direction finding (DF) methods like MUltiple SIgnal Classification (MUSIC). These methods require precise knowledge of the array response and are sensitive to model perturbations. Self-calibration uses sources of opportunity to estimate both, the unknown directions of arrival (DOAs) as well as the model perturbations.In this paper we propose a new technique that combines the aforementioned self-calibration and the DPD approach for a single moving array sensor. By fully exploiting the source position, gain and phase imperfections can be uniquely determined, using a single source of opportunity. We derive the Cramér-Rao lower bound for the problem of joint calibration and localization for a deterministic signal model and show that the proposed estimator is asymptotically efficient in our numerical experiments. Finally, the proposed technique is verified using measurements collected during field trials.