Single-Sample Direction-of-Arrival Estimation for Fast and Robust 3D Localization With Real Measurements from a Massive MIMO System
Stepan Mazokha, Sanaz Naderi, Georgios I. Orfanidis, George Sklivanitis, Dimitris A. Pados, Jason O. Hallstrom
Abstract
Fast, robust, high-accuracy localization is a key enabler for future location-aware applications in streetscape communication networks and next-generation networked autonomous agents. Specifically, massive multiple-input and multiple-output (MIMO) antenna systems have received increasing attention due to high angular resolution. However, in dense multipath environments, such as urban areas, pure direction-of-arrival (DoA)-based techniques have not been very popular due to large localization errors.In this paper, we present and evaluate, on real measurements from the POWDER-RENEW platform, a novel method to carry out DoA estimation from just one antenna array snapshot. The measurements are taken from an indoor testbed that is based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system. Experimental results – in the presence of spatial aliasing – show that for certain emitter locations our proposed universal one-shot DoA estimator outperforms in azimuth/elevation accuracy state-of-the-art subspace-based methods that involve collection of a sufficiently large data record of antenna array snapshots.
BibTeX
@inproceedings{icassp2023_singlesampledire,
title = {Single-Sample Direction-of-Arrival Estimation for Fast and Robust 3D Localization With Real Measurements from a Massive MIMO System},
author = {Stepan Mazokha and Sanaz Naderi and Georgios I. Orfanidis and George Sklivanitis and Dimitris A. Pados and Jason O. Hallstrom},
booktitle = {ICASSP 2023},
year = {2023}
}