机器学习在惯性传感中的应用研究进展OA

With the rapid development of microelectromechanical systems(MEMS)technology,inertial sensors have been widely used in military,civil and special fields(e.g.,deep-sea exploration,seismic monitoring)due to their advantages of small size,low power con-sumption and light weight.The progress of machine learning application in inertial sensing is systematically reviewed,focusing on its ap-plication in key technologies such as sensor calibration and error compensation,multi-sensor information fusion,navigation and localisa-tion,motion recognition and behaviour analysis.By analysing algorithms such as support vector machines,neural networks and reinforce-ment learning,the remarkable effectiveness of machine learning in enhancing the accuracy,robustness and adaptability of inertial sensing is summarised.By providing a comprehensive overview of the application of machine learning in inertial sensing,readers from di-verse backgrounds,including researchers and practitioners interested in the potential of machine learning-based techniques on enhancing inertial sensing,are appealed to promote the further development of inertial sensing technology.Finally,the advantages and challenges of machine learning-based inertial sensing are summarised and future research directions are proposed.