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Research on Adaptive Target Tracking Method Based on Millimeter Wave Radar

Published: 02 October 2021 Publication History

Abstract

This paper introduces a target-tracking algorithm based on the millimeter-wave radar, to detect the number and positions of the indoor individuals. The proposed algorithm combines the traditional DBSCAN clustering algorithm and GMM clustering algorithm, which can adapt to the closely-distributed targets situation, with better clustering capability. Through experiments, the point cloud data obtained by the millimeter wave radar has been processed to identify the target with the proposed algorithm. Compared with the traditional DBSCAN algorithm and the GMM algorithm, the recognition result of the fusion algorithm has better robustness and can distinguish effective point from the clouds and clutter, to further accurately locate the indoor target.

References

[1]
Yavari E, Song C, Lubecke V, Is there anybody in there?: Intelligent radar occupancy sensors[J]. IEEE Microwave Magazine, 2014, 15(2): 57-64.
[2]
Sun Y, Hang R, Li Z, Privacy-preserving fall detection with deep learning on mmWave radar signal[C]//2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019: 1-4.
[3]
Zhou J, Jin Z and Chen P.Fire detection and rescue method and system based on millimeter wave radar technology[P]. Patent, CN110058220A, 2019.
[4]
Chen B, Lei Y. Indoor and outdoor people detection and shadow suppression by exploiting HSV color information[C]//The Fourth International Conference onComputer and Information Technology, 2004. CIT'04. IEEE, 2004: 137-142.
[5]
Vaidehi V, Ganapathy K, Mohan K, Video based automatic fall detection in indoor environment[C]//2011 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE, 2011: 1016-1020.
[6]
Diaz R, Chan S C, Liu J M. Lidar detection using a dual-frequency source[J]. Optics letters, 2006, 31(24): 3600-3602.
[7]
Zheng X, Wang J, Shangguan L, Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures[C]//IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 2016: 1-9.
[8]
Mukhopadhyay B, Srirangarajan S, Kar S. Modeling the analog response of passive infrared sensor[J]. Sensors and Actuators A: Physical, 2018, 279: 65-74.
[9]
Benezeth Y, Laurent H, Emile B, Towards a sensor for detecting human presence and characterizing activity[J]. Energy and Buildings, 2011, 43(2-3): 305-314.
[10]
Jang J, Hwang S, Park K. Unambiguous range extension of a phase-shift based lidar by using two laser diodes with different modulation frequencies[C]. International Conference on Optics in Precision Engineering and Nanotechnology (icOPEN2013). International Society for Optics and Photonics, 2013, 8769: 87693A.
[11]
Almasi M A, Mehrpouyan H, Vakilian V, Reconfigurable antennas in mmWave MIMO systems[J]. arXiv preprint arXiv:1710.05111, 2017.
[12]
Huang X, Cheena H, Thomas A, Indoor Detection and Tracking of People Using mmWave Sensor[J]. Journal of Sensors, 2021.
[13]
Zhang R., Cao S. In Robust and Adaptive Radar Elliptical Density-Based Spatial Clustering and labeling for mmWave Radar Point Cloud Data, 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019.
[14]
Zhang Z., Zhang L., Tong X., A Multilevel Point-Cluster-Based Discriminative Feature for ALS Point Cloud Classification. IEEE Transactions on Geoscience & Remote Sensing 2016, 54 (6), 3309-3321.

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      cover image ACM Other conferences
      ACM TURC '21: Proceedings of the ACM Turing Award Celebration Conference - China
      July 2021
      284 pages
      ISBN:9781450385671
      DOI:10.1145/3472634
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 02 October 2021

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      Author Tags

      1. DBSCAN clustering
      2. GMM clustering
      3. Millimeter wave radar
      4. Target detection

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