Multiple sources localization with 2D-DFT under distributed massive antenna arrays

Traditional localization approaches, e.g., angle of arrival clustering (AOA-clustering), subspace data fusion, and minimum variance distortionless response, entail eigenvalue decomposition or global grid search within the target area, which could pose significant challenges when applied to massive antenna arrays as a result of their high computational complexity. In this paper, we present a low-complexity localization approach for multiple sources with two-dimensional discrete Fourier transform (2D-DFT). Firstly, we compute the cross-covariance, which could effectively suppress noise and obtain aligned AOAs via 2D-DFT. Then, we utilize phase offset method and total least square solution to obtain accurate position estimates. The proposed approach could achieve a good compromise between performance and computational complexity. The Cramer-Rao Bound (CRB) and simulation results are provided to corroborate the effectiveness and superiority of the proposed approach.

Automated summary

In this paper, a low-complexity localization approach for multiple sources using two-dimensional discrete Fourier transform (2D-DFT) is proposed. The method computes the cross-covariance and utilizes phase offset method and total least square solution to obtain accurate position estimates.