scholarly journals An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

2019 ◽  
Vol 9 (8) ◽  
pp. 1606 ◽  
Author(s):  
Binhan Du ◽  
Jinlong Song ◽  
Zhiyong Shi
Keyword(s):  
Mechanical System ◽  
Inertial Measurement Unit ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Recognition Method ◽  
Detection Function ◽  
Inertial Measurement ◽  
Redundant Data ◽  
Parity Space ◽  
Space Method

The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.

scholarly journals Research on the automotive sensor–aided low-cost inertial navigation system for land vehicles

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401882287 ◽  
Author(s):  
Susu Fang ◽  
Zengcai Wang ◽  
Lei Zhao
Keyword(s):  
Mechanical System ◽  
Navigation System ◽  
Inertial Measurement Unit ◽  
Inertial Navigation System ◽  
Micro Electro Mechanical System ◽  
Wheel Speed ◽  
Measurement Unit ◽  
Angle Sensor ◽  
Inertial Measurement ◽  
Speed Sensor

When a low-cost micro-electro-mechanical system inertial measurement unit is used for a vehicle navigation system, errors will quickly accumulate because of the large micro-electro-mechanical system sensor measurement noise. To solve this problem, an automotive sensor–aided low-cost inertial navigation system is proposed in this article. The error-state model of the strapdown inertial navigation system has been derived, and the measurements from the wheel speed sensor and steer angle sensor are used as the new observation vector. Then, the micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated system is established based on adaptive Kalman filtering. The experimental results show that the positioning error of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor is 94.67%, 98.88%, and 97.88% less than the values using pure strapdown inertial navigation system in the east, north, and down directions, respectively. The yaw angle error is reduced to less than 1°, and the vehicle velocity estimation of micro-electro-mechanical system inertial measurement unit/wheel speed sensor/steer angle sensor–integrated navigation system is closer to the reference value. These results show the precision of the integrated navigation solution.

A Low-Cost MEMS Implementation Based on Sensor Fusion Algorithms

2015 ◽  
Vol 738-739 ◽  
pp. 42-45
Author(s):  
Xian Wei Wang ◽  
Jun Hai Jiang
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Output Signal ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
6 Degrees Of Freedom

In this paper a low-cost Micro-Electro-Mechanical System (MEMS) inertial measurement unit is designed, a 3-axis accelerometer and 3-axis gyroscope simulated 6 degrees of freedom orientation sensing through sensor fusion. By analyzing a simple complimentary filter and a more complex Kalman filter, the outputs of each sensor were combined and took advantage of the benefits of both sensors to improved results. The experimental results demonstrate that the output signal can be corrected suitability by means of the proposed method.

MEMS Gyroscope Null Drift and Compensation Based on Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 2077-2081 ◽  
Author(s):  
Jaw Kuen Shiau ◽  
Der Ming Ma ◽  
Chen Xuan Huang ◽  
Ming Yu Chang
Keyword(s):  
Neural Network ◽  
Inertial Measurement Unit ◽  
Temperature Compensation ◽  
Micro Electro Mechanical System ◽  
Measurement Unit ◽  
Temperature Calibration ◽  
Inertial Measurement ◽  
Mems Gyroscope ◽  
Measurement Units ◽  
Effects Of Temperature

This study investigates the effects of temperature on micro-electro mechanical system (MEMS) gyroscope null drift and methods and efficiency of temperature compensation. First, this study uses in-house-designed inertial measurement units (IMUs) to perform temperature effect testing. The inertial measurement unit is placed into the temperature control chamber. Then, the temperature is gradually increased from 25 °C to 80 °C at approximately 0.8 degrees per minute. After that, the temperature is decreased to -40 °C and then returning to 25 °C. During these temperature variations, the temperature and static gyroscope output observes the gyroscope null drift phenomenon. The results clearly demonstrate the effects of temperature on gyroscope null voltage. A temperature calibration mechanism is established by using a neural network model. With the temperature calibration, the attitude computation problem due to gyro drifts can be improved significantly.

scholarly journals Smartphone Heading Correction Based on Gravity Assisted and Middle Time Simulated-Zero Velocity Update Method

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3349 ◽  
Author(s):  
Qinghua Zeng ◽  
Shijie Zeng ◽  
Jianye Liu ◽  
Qian Meng ◽  
Ruizhi Chen ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Micro Electro Mechanical System ◽  
Walking Distance ◽  
Measurement Unit ◽  
Error Diffusion ◽  
Correction Algorithm ◽  
Inertial Measurement ◽  
Filtering Algorithm ◽  
Zero Velocity ◽  
Gravity Sensor

Electronic appliances and ferromagnetic materials can be easily found in any building in urban environment. A steady magnetic environment and a pure value of geomagnetic field for calculating the heading of the smartphone in case of pedestrian walking indoors is hard to obtain. Therefore, an independent inertial heading correction algorithm without involving magnetic field but only making full use of the embedded Micro-Electro-Mechanical System (MEMS) Inertial measurement unit (IMU) device in the smartphone is presented in this paper. Aiming at the strict navigation requirements of pedestrian smartphone positioning, the algorithm focused in this paper consists of Gravity Assisted (GA) and Middle Time Simulated-Zero Velocity Update (MTS-ZUPT) methods. With the help of GA method, the different using-mode of the smartphone can be judged based on the data from the gravity sensor of smartphone. Since there is no zero-velocity status for handheld smartphone, the MTS-ZUPT algorithm is proposed based on the idea of Zero Velocity Update (ZUPT) algorithm. A Kalman Filtering algorithm is used to restrain the heading divergence at the middle moment of two steps. The walking experimental results indicate that the MTS-ZUPT algorithm can effectively restrain the heading error diffusion without the assistance of geomagnetic heading. When the MTS-ZUPT method was integrated with GA method, the smartphone navigation system can autonomously judge the using-mode and compensate the heading errors. The pedestrian positioning accuracy is significantly improved and the walking error is only 1.4% to 2.0% of the walking distance in using-mode experiments of the smartphone.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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