A Detection Algorithm for Discovering Accompanying Relationship of Cruise Passengers Based on UWB Positioning

UWB positioning is used in the cruise to carry out an on-board personnel location experiment to discover the accompanying relationship among passengers in the interior space of a cruise. A improved scheme based on Haussdorff-DBSCAN is proposed combined with indoor positional semantics to study the clustering of the passenger trajectories, based on the characteristics of the UWB location data. Afterward, the LSTM neural network is applied to predict the changing similarity of the suspected companions. The traditional Hausdorff algorithm does not consider the consistency of trajectory timing while calculating the trajectory similarity, and the introduction of positional semantic sequence can solve this problem. In the first phase, the passenger trajectory data set is input to the improved Hausdorff-DBSCAN algorithm, and the clustering radius is determined according to the overall similarity threshold of trajectories. The outputs are the emerging clusters of passenger trajectories in the same companion group. In the second phase, the LSTM neural network takes the point similarity sequence with a fixed time window as the input to predict the point similarity value at the next time. The sequential change of passengers accompanying relationship is analyzed by the similarity threshold and predicted results. The validity of the presented algorithm is demonstrated by the trajectory data obtained from the simulation study, which is modeled in Anylogic. The results indicate that the precision of the proposed algorithm, the recall value, and the F₁ value are 0.92, 0.95, and 0.934, which are at least 5.7%, 8.0%, and 6.7% higher than the compared algorithm, respectively. The LSTM neural network also shows a promising effect in predicting the changing trend of the similarity because the loss is at a stable level from 3% to 4%.