Fundamental Limits of Direction Finding in Distributed Arrays Exploiting Auxiliary Sources

We consider the problem of estimating the directions of multiple target sources by exploiting auxiliary sources, focusing on a single snapshot obtained by the distributed array with position errors and angular offsets of subarrays. Former calibration methods generally assume the directions of auxiliary sources are unknown, while prior knowledge of the auxiliary sources is usually available in practice. In order to quantify the effects of auxiliary sources and their prior information, we model the directions of auxiliary sources as Gaussian random variables and use their standard deviations to quantify the prior information. We derive the prior Cramér-Rao lower bound (CRB) of the direction estimations in the new model. Simulation results show that calibration with auxiliary sources performs better than self-calibration and the prior CRB is much lower than the existing counterparts assuming unknown auxiliary sources, implying much potential to improve the estimation performance by employing the prior information.