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.

中文翻译：

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MEMS惯性传感器精度提高的随机误差减少算法—综述

研究和工业研究表明，小尺寸，低成本，高精度和易于集成是微电子机械系统（MEMS）惯性传感器在批量生产和各种应用中的重要特征。近年来，已经在越来越广泛的应用范围中寻求诸如MEMS加速度计和MEMS陀螺仪之类的传感器，例如用于国防和军事武器的医疗保健的医疗设备。MEMS惯性传感器的一个重要局限性是反复记录的，因为它容易受到随机来源的环境噪声，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，随机误差处理对于适当消除伪像信号以及提高这种传感器的准确性和可靠性至关重要。在本文中，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审查。为此，在几个数据库（Web of Science，IEEE Xplore，Science Direct和计算机科学数字图书馆协会）上进行了深入的文献搜索。下载并仔细审查了四十九篇代表性论文，这些论文专注于处理来自MEMS加速度计，MEMS陀螺仪和MEMS惯性测量单元的信号，并以期刊或会议的形式发表，并在过去十年中在数据库中建立了索引。从该文献的概述中，提取了30种主流算法并将其归类为七个组，对其进行了分析以展示其贡献，优势，和文学的弱点。此外，还介绍了研究中开发的模型的摘要，以及它们的工作原理（即应用领域）以及研究中得出的结论。最后，介绍了MEMS惯性传感器技术的发展趋势及其应用前景。