Localization for mixed near-field and far-field sources using data supported optimization

Recently, localization for the coexistence of the far-field and near-field sources has received more attentions. In this paper, a maximum likelihood (ML) localization method using data supported optimization is considered. The range and direction of arrival (DOA) of the sources are estimated sequentially. Since a two step estimation method is used, the proposed method is applicable for the near-field sources, far-field sources or the mixture of these two kinds of sources. Furthermore, the proposed method is applicable for far-field and near-field source classification. Simulations are implemented to verify the performance of the proposed method.