LAUSR

Due to the capability to separate the line-of-sight (LOS) signal from multipath signals in both time-space domains, the joint azimuth, elevation angle, and time delay (TD) estimation technique is of great importance in the Internet of Things. However, as mainstream devices develop toward the large-scale multiple-input and multiple-output (MIMO) system, real-time processing gradually becomes computationally impractical for traditional element-space 3-D joint angle and delay estimation (JADE) methods. In this article, based on the measured channel state information (CSI) acquired from a large-scale uniform rectangular array (URA)-orthogonal frequency division multiplexing (OFDM) system, a computationally efficient 3-D beamspace JADE algorithm is proposed. Firstly, we develop a method to select the beam that contains the LOS path as the optimal beam. Then, for the parameter estimation, we transform the CSI into the beamspace by utilizing the discrete Fourier transform (DFT) sequence, and propose a modified 3-D beamspace matrix pencil (BMP) algorithm only with the optimal beam and its adjacent beams, which contributes to conspicuous computational savings. Moreover, the estimation of delay, elevation, and azimuth for the LOS path are paired automatically with only one eigenvalue decomposition (EVD), and the multidimensional grid search is avoided. Experiment results demonstrate that the proposed approach could correctly select the optimal beam with high probability, and its parameter estimation accuracy is superior to the state-of-the-art JADE techniques while significantly reducing the computational complexity.