High-resolution direct position determination based on eigenspace using a single moving ULA

High-resolution direct position determination (DPD) using a single moving uniform linear array is considered. In this paper, we firstly propose an improved DPD model based on eigenspace. This model exploits both signal subspaces and noise subspaces which results in higher resolution than those utilizing minimum variance distortionless response, multiple signal classification or subspace fitting. In order to achieve rapid and high precision localization, a hybrid calculation algorithm which combines particle swarm optimization using a ring topology and Broyden–Fletcher–Goldfarb–Shanno is proposed. This algorithm can extract multiple emitter positions with less computational complexity. We combine the improved DPD model and the hybrid calculation algorithm and examine its performance via numerical simulations. The results show that the proposed method can reach Cramer–Rao lower bound when the signal-to-noise ratio is moderate.