Name
Title | ||
|---|---|---|
MD-Alarm: A Novel Manpower Detection Method for Ship Bridge Watchkeeping Using Wi-Fi Signals |
Abstract | ||
|---|---|---|
Sufficient manpower on ship bridges plays an important role in ship navigation and watchkeeping safety. Effectively counting the number of watchkeeping officers and evaluating compliance with manning rules can prevent many ship accidents caused by fatigue and poor lookouts. Current solutions mostly rely on surveillance cameras and watchmen to determine whether to report to the shipmaster, a system that requires extra human effort and raises concerns regarding officers' privacy. In this article, we utilize Wi-Fi infrastructure in a ship bridge and propose a Wi-Fi -signal-based watchkeeping officer counting method based on fine-grained channel state information (CSI) instead of using cameras. To achieve this goal, we first adopt the Pearson correlation coefficient to evaluate the sensitivity of CSI and extract the CSI subcarriers (SCs) that are most sensitive to human activities, rather than environmental changes. Then, an expansion matrix algorithm is used to estimate the number of people in the line-of-sight (LOS) Wi-Fi link using the extracted CSI SCs, and a multilink fusing scheme is proposed to determine the total number of officers in the bridge by using multiple Wi-Fi devices. Finally, after obtaining the number of current watchkeeping officers, combined with the current spatiotemporal properties of the ship, the system determines the number of safe navigation personnel. If the number of people detected does not meet the requirements, the system will issue an alarm. We have conducted extensive experiments on a real-world passenger ship. The experimental results show that after introducing the sensitive SC extraction module, the accuracy of counting the number of officers increased by at least 17%. When the total number was less than 4 or equal to 1, the system achieved an average accuracy of 88% and 94%, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIM.2022.3141158 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
Keywords | DocType | Volume |
Marine vehicles, Bridges, Wireless fidelity, Navigation, Wireless sensor networks, Feature extraction, Personnel, Channel state information (CSI), crowd counting, ship bridge watchkeeping, wireless sensing | Journal | 71 |
ISSN | Citations | PageRank |
0018-9456 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengda Chen | 1 | 0 | 0.34 |
| Jie Ma | 2 | 0 | 0.68 |
| Xuming Zeng | 3 | 0 | 1.01 |
| Kezhong Liu | 4 | 0 | 0.34 |
| Mozi Chen | 5 | 1 | 1.02 |
| Kai Zheng | 6 | 0 | 0.34 |
| Kehao Wang | 7 | 0 | 0.34 |