A Machine Learning Approach for Dead-Reckoning Navigation at Sea Using a Single Accelerometer

Dead-reckoning (DR) navigation is used when Global Positioning System (GPS) reception is not available or its accuracy is not sufficient. At sea, DR requires the use of inertial sensors, usually a gyrocompass and an accelerometer, to estimate the orientation and distance traveled by the tracked object with respect to a reference coordinate system. In this paper, we consider the problem of DR navigation for vessels located close to or on the sea surface, where motion is caused by ocean waves. In such cases, the vessel pitch angle is fast time varying and its estimation by direct measurements of orientation is prone to drifts and noises of the gyroscope. Regarding this problem, we propose a method to compensate for the vessel pitch angle using a single acceleration sensor. Using a constraint expectation-maximization (EM) algorithm, our method classifies acceleration measurements into states of similar pitch angles. Subsequently, for each class, we project acceleration measurements into the reference coordinate system along the vessel heading direction, and obtain distance estimations by integrating the projected measurements. Results in both simulated and actual sea environments demonstrate that, by using only acceleration measurements, our method achieves accurate results.