Most exiting array signal processing techniques for bearing estimation are strongly relied on the far-field assumption. When the sources are located close to the array, these techniques may no longer perform satisfactorily. In this work, we propose a tensor-based algorithm which is dedicated to the joint estimation of the range and the bearing of multiple narrow-band and near-filed sources in a spatially white Gaussian noise. Automatic paring of the model parameters is achieved for an uniform linear array. By means of numerical simulation, we show that for low Signal To Noise Ratio, the proposed algorithm is more accurate than the Higher Order Statistics (HOS)- based ESPRIT algorithm for small/moderate number of snapshots.