New Subspace-Based Method for Localization of Multiple Near-Field Signals and Statistical Analysis

This paper investigates the localization of multiple near-field narrowband signals impinging on a symmetrical uniform linear array (ULA), and a new computationally efficient subspace-based method is proposed. The directions-of-arrival (DOAs) and ranges are estimated separately with a one-dimensional (1-D) subspaced-based estimation technique without eigendecomposition, where the null spaces are obtained through a linear operation of the matrices formed from the anti-diagonal elements of the noiseless array covariance matrix, and the estimated DOAs and ranges are automatically paired without any additional processing. Furthermore, the statistical analysis of the proposed method is studied, and the asymptotic mean-square-error (MSE) expressions of the estimation errors are derived. The effectiveness and the theoretical analysis of the proposed method are verified through numerical examples, and the simulation results show that our method provides good estimation performance for both the DOAs and ranges.