A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

TL;DR

This survey reviews indoor mmWave device-based localization and device-free sensing technologies, highlighting recent advances, challenges, and future research directions for high-accuracy indoor positioning and sensing applications.

Contribution

It provides a comprehensive overview of mmWave localization and sensing techniques, analyzing algorithms, hardware platforms, and implementation status, and discusses promising future research avenues.

Findings

mmWave technologies enable high-accuracy indoor localization and sensing

current algorithms and hardware have limitations in dense deployments

machine learning and data fusion are promising research directions

Abstract

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.