Journal Article

Four-Dimensional Parameter Estimation for Mixed Far-Field and Near-Field Target Localization Using Bistatic MIMO Arrays and Higher-Order Singular Value Decomposition

Jiang, Hong, Zhang, Qi, Zheng, Huiming

2024

Overview

In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cramér–Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method.

• Publisher: MDPI AG
• Subject: Algorithms / Arrays / Covariance / Cramer-Rao bounds / Decomposition / Far fields / Fourier transforms / higher-order singular value decomposition (HOSVD) / Localization / Lower bounds / mathematical models / Methods / multidimensional parameter estimation / Near fields / near-field and far-field / Numerical analysis / Parameter estimation / remote sensing / Simulation methods / Singular value decomposition / Sparsity / Subspaces / target localization / tensor / Tensors