HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

Yang Li; You Li; Yanping Wang; Yun Lin; Wenjie Shen; Wen Jiang; Jinping Sun

doi:10.3390/rs15194698

Remote Sensing (Sep 2023)

HCM-LMB Filter: Pedestrian Number Estimation with Millimeter-Wave Radar in Closed Spaces

Yang Li,
You Li,
Yanping Wang,
Yun Lin,
Wenjie Shen,
Wen Jiang,
Jinping Sun

Affiliations

Yang Li: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
You Li: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Yanping Wang: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Yun Lin: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Wenjie Shen: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Wen Jiang: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Jinping Sun: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

DOI: https://doi.org/10.3390/rs15194698
Journal volume & issue: Vol. 15, no. 19
p. 4698

Abstract

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The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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