Towards Fine-Grained Indoor Localization based on Massive MIMO-OFDM System: Experiment and Analysis
| Autor: | Emmeric Tanghe, Sofie Pollin, Sibren De Bast, Wout Joseph, Chenglong Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
Technology and Engineering Computer science Orthogonal frequency-division multiplexing MIMO Linear Massive multiple-input and Multiplexing Antenna measurements components FOS: Electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Electrical Engineering and Systems Science - Signal Processing Instrumentation Computer Science::Information Theory Measurement Sensors orthogonal Location awareness MIMO-OFDM channel state information (CSI) multiple-output (MIMO) indoor localization fingerprinting machine learning Computer engineering antenna arrays Channel state information frequency-division multiplexing (OFDM) Antennas multipath Antenna (radio) Massive MIMO Multipath propagation Communication channel |
| Zdroj: | IEEE SENSORS JOURNAL |
| ISSN: | 1530-437X 1558-1748 |
| DOI: | 10.48550/arxiv.2103.14863 |
| Popis: | Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (large-scale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectation-maximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of the varying antenna topologies, the size of the training set, the number of antennas, and the effective signal-to-noise ratio (SNR). The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution. |
| Databáze: | OpenAIRE |
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