Han Shipeng, Meng Zhen, Omisore Olatunji, Akinyemi Toluwanimi, Yan Yuepeng
Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Micromachines (Basel). 2020 Nov 21;11(11):1021. doi: 10.3390/mi11111021.
Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.
研究和工业研究表明，体积小、成本低、精度高和易于集成是微机电系统（MEMS）惯性传感器的关键特性，这些特性使其适用于大规模生产和各种应用。近年来，诸如MEMS加速度计和MEMS陀螺仪之类的传感器在越来越广泛的应用领域中得到了应用，如用于医疗保健的医疗设备到国防和军事武器。MEMS惯性传感器的一个重要局限性被反复记录为容易受到来自随机源的环境噪声、底层系统中的机械和电子伪像以及其他随机噪声的影响。因此，随机误差处理对于正确消除伪像信号以及提高此类传感器的精度和可靠性至关重要。本文通过研究最近为提高MEMS惯性传感器精度而开发的不同随机误差信号处理模型，进行了系统的综述。为此，在几个数据库，即科学网、IEEE Xplore、科学Direct和美国计算机协会数字图书馆上进行了深入的文献检索。下载并仔细审查了49篇代表性论文，这些论文以期刊或会议形式发表，聚焦于MEMS加速度计、MEMS陀螺仪和MEMS惯性测量单元的信号处理，且在过去10年内被数据库索引。从这篇文献综述中，提取了30种主流算法并将其分为七组，对这些算法进行分析以展示文献的贡献、优点和缺点。此外，还介绍了研究中开发的模型的总结，以及它们的工作原理，即应用领域和研究得出的结论。最后，介绍了MEMS惯性传感器技术的发展趋势及其应用前景。
