A shipboard integrated navigation algorithm based on smartphone built-in GNSS/IMU/MAG sensors

The rapid development of built-in sensors in smartphones has inspired a variety of ubiquitous public navigation applications. Nevertheless, previous works have focused on vehicle and pedestrian navigation by smartphones, with limited research on shipboard navigation. In this contribution, we designed a multi-sensors fusion navigation algorithm that integrates the global navigation satellite systems (GNSS), inertial measurement unit (IMU), and magnetometer (MAG) to achieve high-precision horizontal positioning on shipboard for smartphones. Considering the changeable motion state of a ship and the setting of non-holonomic constraint (NHC) noise, a magnetometer-based turning detection method is proposed, and a NHC model with adaptive noise is constructed based on the turning detection results. We conducted a lake experiment in Taku Forts Lake, Tianjin, using sensor data collected from a Xiaomi MI8 through our self-developed 'Sensor Logger' app. The experimental results demonstrated a high success rate of 94.22 % for magnetometer-based turning detection which can effectively detect turning motions. Compared to the traditional algorithm without constraint information, the roll, pitch and yaw accuracy improve by approximately 34.7 %, 17.9 %, and 57.9 %, respectively. Additionally, the position and velocity accuracy in the east and north directions improve to 0.0926 m, 0.1779 m, 0.0548 m/s, and 0.0589 m/s, respectively, representing enhancements of about 10.7 %, 7.4 %, 10.5 %, and 6.4 %. Overall, the proposed algorithm efficiently improves the shipboard navigation accuracy of smartphones, especially in terms of attitude accuracy. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.