A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Abstract

The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (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 overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used.We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standardcompliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Manuscript received xxxx xx, xxxx . . .

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

“…The most related to our paper is [12], which summarized the state-of-the-art in device-based localization and devicefree sensing using mmWave communication and radar devices. In the device-free sensing section, that paper concentrates on the relevant signal processing techniques and learning techniques.…”

“…There have been some related surveys that provide a summary of particular scopes, such as mmWave-based communication [8], [9], application scenarios [10], [11] and sensing techniques [12]- [14]. We compare and summarize them in detail in the next section.…”

SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems

“…As commonly done in JCS systems, we assume that the LOS distance, d LOS , and the relative orientation between the transmitter and the receiver, α, are known a priori. d LOS can be estimated by applying localization methods to the receiver node, for example, [35], while α is typically obtained from a beam alignment protocol [36]. Unlike existing approaches, we are interested in tracking both the dynamic and the static multipath components in the CIR, as the latter are used to remove the FO.…”

Radio Localization and Sensing—Part I: Fundamentals

“…Resolution is the ability to separate correlated signals. For example, if two objects, say, l and l , in (1) have similar AoA, AoD, ToA, and Doppler, they would appear to the receiver as 5 In most practical cases, the expression is simplified by using the Slepian-…”

“…In this letter, sensing will refer to state estimation of a passive object in the frame of reference of the sensor (see Fig. 1-(b,c)), and thus includes radar [4] and device-free localization [5].…”