mmCTD: Concealed Threat Detection for Cruise Ships via mmWave Radar

The safeguarding of critical zones aboard a marine vehicle, such as the engine room, wheelhouse, and pump room, assumes crucial significance while navigating through the open sea. Despite the existing pre-boarding security measures, Concealed Threat Detection (CTD) systems have emerged as a pressing...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2024, Vol.73 (12), p.18434-18451
Main Authors: Pei, Dashuai, Gong, Danei, Liu, Kezhong, Zeng, Xuming, Zhang, Shengkai, Chen, Mozi, Zheng, Kai
Format: Article
Language:English
Subjects:
Concealed threat detection (CTD)
Cruise ship
Ghost target
Imaging
Marine vehicles
Millimeter wave communication
MmWave radar
Radar
Radar detection
Radar imaging
Radio frequency
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Summary:The safeguarding of critical zones aboard a marine vehicle, such as the engine room, wheelhouse, and pump room, assumes crucial significance while navigating through the open sea. Despite the existing pre-boarding security measures, Concealed Threat Detection (CTD) systems have emerged as a pressing need to prevent the ship from post-boarding damage with concealed dangers. Due to concerns regarding deployment cost and privacy, mmWave-based CTD systems have received significant attention. However, current solutions are not easily adapted to work in ships because of the large number of ghost targets resulting from multipath reflections in full metal cabins. To address these challenges, this paper proposes a new CTD system, called mmCTD, which utilizes two mmWave commercial radars. The proposed system addresses the multipath challenge by unifying multi-view perceptions with two distinct designs. First, we propose a ghost-point elimination algorithm that extracts the point clouds from real objects. Then, we design a multi-view domain adversarial framework to predict concealed threats in the human body using the extracted RF features. mmCTD is validated by both simulations and real ship experiments, and results demonstrate that the recognition accuracy in three scenarios reaches 89% with a low false alarm rate.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3352039