In ocean voyages, cabin indoor localization plays a considerably important role in ship safety management. Due to the special structure of ships and the influence of internal and external environment, the mainstream indoor positioning technology nowadays is not ideal for positioning inside the cabin. In order to improve the positioning accuracy, this article proposes an adaptive K-value-based cabin indoor positioning algorithm. The algorithm constructs a fingerprint library by collecting Received Signal Strength Indication data and carries out filtering; matches fingerprint information by adaptive K-values during the localization process, while using double K-nearest neighbor to reduce the influence of outlier points on the final localization; and finally uses the multi-point mass center method to determine the final localization results. The experimental results in the automated cabin laboratory show that the average positioning error of the algorithm is 1.74 m, which can well meet the cabin indoor positioning requirements.

In the process of ship's ocean transportation, the monitoring of crew's work completion is a very important part in the ship safety management, and using indoor positioning technology can improve the construction of ship management system. However, the current indoor positioning technology is not ideal in the engine room environment in the face of the complex electromagnetic environment and spatial structure. In order to improve the positioning accuracy, this paper proposes a Bluetooth indoor positioning strategy for ship cabin based on hierarchical area (BSSH) for deploying and dividing the regions requiring indoor positioning and combines Bluetooth Low Energy (BLE) with Bluetooth fingerprint positioning technology. Processing the fingerprint library by using region hierarchy and proximity classification algorithms to obtain reliable results in the ship environment, the results show that better Beacon deployment and hierarchical area algorithms can lead to a localization accuracy of 80.38%.