基于信道状态信息的船舶驾驶台人员检测及活跃度评价方法

船舶驾驶台人员包括按照规定要求的常规值班人员和特殊情况下额外的瞭望人员或船长、引航员等，驾驶台人员活跃度是判断其工作状态的重要指标之一。传统的基于计算机视觉的人员检测方法在面对船舶驾驶台遮挡物多、夜间或恶劣天气下光线不足等问题时，精度明显降低。为解决该问题，提出了1种基于普通商用Wi-Fi设备的活跃度感知方法。由于船体材质、结构特点以及变化的运动状态导致动态多径多、信号噪声强，对Wi-Fi设备造成干扰，为此设计了值班高关联度数据（duty high correlation data，DHCD）选择模块及基于信道状态信息（channel state information，CSI）的多层级特征提取模块。DHCD选择模块分析驾驶台人员不同航行、值班情况下的CSI特点，对比0~5人在驾驶台内值班、工作时的信道变化，利用模糊C-means聚类算法提取CSI中对值班人员行为反应最灵敏的信道，去除对信号噪声反应敏感的信道信息；通过多层级特征提取模块计算去噪后CSI数据的幅值与相位离散度、多链路融合离散度、变异指数等多层特征，作为活跃度评价基础参数。依据驾驶台值班要求设计了驾驶台人员活跃度评价模块，采用支持向量机算法判断驾驶台人员数量，采用客观赋权法得到基础参数权重，结合人数信息与权重信息评价驾驶台人员活跃度。实验结果表明：使用DHCD选择模块和多层级模块处理后的多层级特征将驾驶台人员数量检测精度提升至89.6%，对比直接使用原始数据时检测精度提升7.1%。在夜间、雨雾天气等光照不足情况下，基于计算机视觉方法的检测精度会由光线充足时的96.2%降至60.3%，而该方法监测精度不会降低。因此，基于CSI的驾驶台人员活跃度检测方法丰富了驾驶台人员检测算法，能有效识别船舶驾驶台人员是否符合安全值班的基本要求。

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.