Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Abstract

In this paper, comprehensive mPoint, a method for generating 3D (range, azimuth, and elevation) point cloud of human targets using a Frequency-Modulated Continuous Wave (FMCW) signal and Multi-Input Multi-Output (MIMO) millimeter wave radar is proposed. Distinct from the TI-mPoint method proposed by TI technology, a comprehensive mPoint method considering both the static and dynamic characteristics of radar reflected signals is utilized to generate a high precision point cloud, resulting in more comprehensive information of the target being detected. The radar possessing 60–64 GHz FMCW signal with two sets of different dimensional antennas is utilized in order to experimentally verify the results of the methodology. By using the proposed process, the point cloud data of human targets can be obtained based on six different postures of the underlying human body. The human posture cube and point cloud accuracy rates are defined in the paper in order to quantitively and qualitatively evaluate the quality of the generated point cloud. Benefitting from the proposed comprehensive mPoint, evidence shows that the point number and the accuracy rate of the generated point cloud compared with those from the popular TI-mPoint can be largely increased by 86% and 42%, respectively. In addition, the noise level of multipath reflection can be effectively reduced. Moreover, the length of the algorithm running time is only 1.6% longer than that of the previous method as a slight tradeoff.

Millimeter-Wave Radar Localization Using Indoor Multipath Effect

“…We compared MAL with two of the more advanced existing mm-Wave radar-based localization methods: the point-cloud-based mm-Wave radar localization method (denoted by Point Cloud [ 38 ]), and the conventional mm-Wave radar localization method (denoted by Radar [ 39 ]). Both the results of the IWR1642 millimeter-wave radar development board-acquired data were used for comparison.…”

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

“…A Point cloud image is a group of point images in which signals reflected from objects are produced in the form of three-dimensional points, and have been widely applied to implement a digital twin of buildings or objects using laser scanners [16][17][18][19][20][21].…”

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

“… represents the peak list of the RAI in elevation angle direction, including the range, elevation angle, and human reflected power. Correlate the points of the two planes with the distance value and the power value to obtain the point set of the target three-dimensional space point cloud, the generation method is shown in Equation (4), where represents a fusion of the data of the two planes [ 34 ]. …”

“…In the first stage, the characteristics data of human posture are measured and collected using the radar sensor and then the human body posture point cloud data is generated considering both the dynamic and static features of the reflected signal for the human body. Based on the previous research method [ 34 ], six hundred sets of point cloud posture data were obtained from one hundred sets of point cloud data for each of the six postures (hands up, horse stance, lunge, lying down, standing, and sitting). In the second stage, the point cloud dataset is used for six machine learning classification models, namely K-nearest neighbor (KNN), Gaussian process (GP), SVM, multi-layer perceptron (MLP), naive Bayes (NB), and gradient boosting (GB).…”