Deep-Learning-Based Wireless Human Motion Tracking for Mobile Ship Environments

Abstract

Being able to track passengers’ movement without invasion of their privacy plays an important role in cruise ships; it enables crucial location-based services, such as maritime search and rescue, tourist services, and epidemic prevention. The past few years have witnessed commodity WiFi holding great potential that provides such services available thanks to its ubiquitous in indoor scenarios. However, existing WiFi-based tracking methods suffer from huge performance degradation in sailing ships due to their complex metal structures and dynamic hull deformation caused by engines and waves/payloads pressure. In this article, we present CRLoc, a deep learning-based passive human tracking system that can overcome the practical limitations of traditional WiFi-based localization approaches applied in a multipath-rich and mobile ship environment, and provide decimeter-level tracking accuracy in cruise ships. Specifically, we make two contributions, i.e., we propose a super-resolution parameter estimation algorithm that better characterizes ship indoor environments, and a deep neural network-based end-to-end solution to remove the impact of noise, interference, and mobility in ships. The real-world implementation and extensive experiments in several passenger ships demonstrate that CRLoc tracks human motions with a median error of 92 cm, better than state-of-the-art localization methods. To our knowledge, this is one of the first WiFi-based passive human motion tracking system in a cruise ship environment.