Abstract
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.
Highlights
The microelectromechanical systems (MEMS) inertial sensor is an instrument that is used to measure angular velocity and acceleration [1,2]
From the 49 reviewed articles, 30 random error signal processing algorithms were summatitle and namely, abstract, totalsimple of 256 rized, and they were divided into sevenRead groups filter algorithms, Kalman-based algorithms, wavelet-based algorithms, sensor fusion
It is vital to state that all these algorithms were aimed at reducing raw signal error for MEMS inertial sensor precision improvement
Summary
The microelectromechanical systems (MEMS) inertial sensor is an instrument that is used to measure angular velocity and acceleration [1,2]. MEMS inertial sensors are referred to as MEMS gyroscopes and MEMS accelerometers, and are mainly composed of a micromechanical sensing part, signal processing circuits, and a microprocessor part [3,4,5]. MEMS inertial sensors are commonly employed in high-end markets such as optoelectronic devices, aerospace, torpedo, missile, rockets, and so on [15,16,17]. Until now, compared with laser gyroscopes, fiber gyroscopes, mechanical gyroscopes, and accelerometers, the high-end application range of the MEMS inertial sensor is still limited [18,19]. A reason for11,the huge difference between the application fields of the inertial sensors is mainly
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