Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Bhardwaj, Renu; Kumar, Neelesh; Kumar, V. K.

doi:10.1177/0142331217708237

Cited by 22 publications

(14 citation statements)

References 53 publications

(58 reference statements)

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“…This device is ushering in a huge market demand, as it has become an important component of different navigation systems, attitude control devices [ 2 ], unmanned aerial vehicles [ 3 ], robot navigation [ 4 , 5 , 6 ], satellite systems [ 7 ], etc. MEMS-IMU system is often affected by various factors characterized on environmental, electronic, and manufacturing noises all leading to random navigation errors when using MEMS-IMU [ 8 , 9 ]. These random errors reduce the accuracy of MEMS-IMUs and as well limit their applications.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

et al. 2021

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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

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Section: Introductionmentioning

confidence: 99%

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

et al. 2021

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“…There are numerous applications for MEMS accelerometers today: they include airbag deployment systems used in the automotive industry, modern cell phones and cameras [17], and many scientific applications, including earthquake detection [18]. MEMS devices have reached high-end tactical-grade for military applications [19]. Several applications seek to measure displacement using accelerometers [20], [21].…”

Section: Motion-detection Subsystemmentioning

confidence: 99%

“…Several applications seek to measure displacement using accelerometers [20], [21]. To do so, a double integration of the data with regard to time is necessary, but sensor noise and drift can quickly lead to large errors in such estimates [17], [19], [22]- [26]. The present use case is less demanding: only the fact that a displacement has occurred needs to be established, but not its magnitude or direction.…”

Section: Motion-detection Subsystemmentioning

confidence: 99%

Verifying Deep Reductions in the Nuclear Arsenals: Development and Demonstration of a Motion-Detection Subsystem for a “Buddy Tag” Using Non-Export Controlled Accelerometers

Glaser

Kütt

2020

IEEE Sensors J.

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Future nuclear arms-control agreements may place numerical limits on items that are difficult to monitor with national technical means, even when complemented with onsite inspections. Such items could include small objects and mobile assets, such as non-deployed nuclear warheads and mobile missile launchers. Typically, this verification task can be addressed with unique identifiers, but standard tagging techniques may be unacceptable in this case due to host concerns about safety and intrusiveness. First proposed in the late 1980s and partially developed by Sandia National Laboratories in the early 1990s, the "Proximity Tag" or "Buddy Tag" concept seeks to overcome these concerns by separating the tag from the treaty accountable item itself. A buddy tag has two key elements: a tamper-indicating enclosure and a motion-detection system designed to detect illicit movements in a stand-down period. As part of this project, we have built a buddy-tag prototype for demonstration and evaluation purposes. This paper reviews the design choices and functionalities of the tag's motion-detection subsystem. We pursue a modular approach for the tag's hardware, built around an Arduino-class microcontroller and a non-export-controlled low-noise accelerometer, and use open-source algorithms for the motion-detection software. We discuss the results of an extensive experimental campaign involving both indoor and outdoor measurements assessing the performance of the tag under real-world environmental conditions.

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“…Presently, the low accuracy of MEMS inertial sensors is the biggest challenge that limits its development. The main reason is that the micro size of the MEMS inertial sensor makes it more vulnerable to environmental changes [ 29 , 30 ]. These uncertain factors can cause various noises, which in turn affect the sensor’s output.…”

Section: Introductionmentioning

confidence: 99%

“…The error sources of MEMS inertial sensors are mainly from mechanical noise, electronic noise, environmental noise, and other random noise sources [ 29 , 31 , 32 ]. This kind of uncertain noise or random error limits the accuracy of the sensor and its applicability in different fields.…”

Section: Introductionmentioning

confidence: 99%

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

et al. 2020

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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.

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Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Cited by 22 publications

References 53 publications

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions

Verifying Deep Reductions in the Nuclear Arsenals: Development and Demonstration of a Motion-Detection Subsystem for a “Buddy Tag” Using Non-Export Controlled Accelerometers

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement—A Review

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