Abstract
Vehicles, as the moving platforms of various activities, have played important roles in modern society. However, the mechanical vibration due to various sources greatly degrades the performance of on-board devices that require high precision. To compensate the vibration, the technical challenges include: 1) the vibration possesses multiple time-varying dominant frequencies; 2) the broad bandwidth; 3) the phase difference between compensating movement and vibration; and 4) realizing real-time (RT) operation. In this article, we propose an AI-aided RT estimation and compensation method to address these challenges. The proposed method consists of two recursive least square-based filters to remove the gyroscope noise and drift, and a long short-term memory-based recursive neural network to remove the phase shift. Applied techniques are all implemented in RT. The method is validated by simulations and RT experiments using vibration data sampled from a real vehicle and achieves a 75% compensation rate, which outperforms existing methods.