4 research outputs found
ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking
Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to
cameras for the pervasive contactless monitoring of people in indoor spaces.
However, commercial mmWave radars feature a limited range (up to - m) and
are subject to occlusion, which may constitute a significant drawback in large,
crowded rooms characterized by a challenging multipath environment. Thus,
covering large indoor spaces requires multiple radars with known relative
position and orientation and algorithms to combine their outputs. In this work,
we present ORACLE, an autonomous system that (i) integrates automatic relative
position and orientation estimation from multiple radar devices by exploiting
the trajectories of people moving freely in the radars' common fields of view,
and (ii) fuses the tracking information from multiple radars to obtain a
unified tracking among all sensors. Our implementation and experimental
evaluation of ORACLE results in median errors of m and for
radars location and orientation estimates, respectively. Fused tracking
improves the mean target tracking accuracy by , and the mean tracking
error is cm in the most challenging case of moving targets. Finally,
ORACLE does not show significant performance reduction when the fusion rate is
reduced to up to 1/5 of the frame rate of the single radar sensors, thus being
amenable to a lightweight implementation on a resource-constrained fusion
center
A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
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.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys &
Tutorials (IEEE COMST
A Review of Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications
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. Device-free (i.e., radar-based) sensing systems still have to be improved in terms of: improved accuracy in the detection of vital signs (respiration and heart rate) and enhanced robustness/generalization capabilities across different environments; moreover, improved support is needed for the tracking of multiple users, and for the automatic creation of radar networks to enable largescale sensing applications. Finally, integrated systems performing joint communications and sensing are still in their infancy: theoretical and practical advancements are required to add sensing functionalities to mmWave-based channel access protocols based on orthogonal frequency-division multiplexing (OFDM) and multi-antenna technologies