A Method for Detecting Personnel at Vessel Bridge and Evaluating Level of Activities Based on Channel State Information

The personnel on bridge consist of regularly scheduled officers on watch and additional person for look-out, captain, and pilots in specific circumstances. The activity level of the personnel on bridge is one of the crucial indicators to assess their work status. Traditional computer vision-based personnel detection methods show reduced accuracy when confronted with challenges such as multiple obstructions on the ship's bridge, insufficient light conditions during nighttime or adverse weather conditions. To address this issue, a detection and activity evaluation method based on ordinary commercial Wi-Fi devices is proposed. Due to the dynamic multipath and strong signal noise caused by the ship's material and structural characteristics and the changing motion states, the function of Wi-Fi devices is interfered. To mitigate these challenges, a duty high-correlation data (DHCD) selection module and a multi-layer feature extraction module based on channel state information (CSI) are designed. The DHCD selection module analyses the CIS characteristics in different navigation and duty situations and compares the channel variations when 0-5 people on the ship's bridge. The fuzzy C-means clustering algorithm is employed to extract the most responsive channel information to the behavior of personnel on bridge while eliminating the information sensitive to signal noise. The multi-layer feature extraction module calculates various features, including amplitude, phase dispersion, multi-link fusion dispersion, and variation index for denoised CSI data as the foundation for activity evaluation. The activity evaluation module is designed primarily based on the requirements for the on-duty personnel on bridge. The Support Vector Machine algorithm is utilized to determine the number of bridge personnel, while the Criteria Importance through the Intercriteria Correlation method is used to obtain the weight for basic parameters. Combining the headcount information and weight information, the activity level of bridge personnel is evaluated. The results indicate that the multi-layer features using the DHCD selection module and multi-layer module processing improve the accuracy of detecting the number of bridge personnel to 89.6%, representing a 7.1% increase compared to directly using raw data. In low-light conditions such as nighttime, rainy, or foggy weather, the accuracy of computer vision-based methods decreases from 96.2% under normal light to 60.3%. In contrast, the detection accuracy of proposed method remains stable. Therefore, the CSI-based detection and activity evaluation method enriches the detection algorithm for bridge personnel and can effectively identify whether the personnel meet the basic requirements for safe duty.