MEMS gyroscope/TAM-integrated attitude estimation based on moving horizon estimation

2016 ◽  
Vol 231 (8) ◽  
pp. 1451-1459 ◽  
Author(s):  
Jingli Huang ◽  
Guorong Zhao ◽  
Xiangyu Zhang
Keyword(s):  
Mechanical System ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Moving Horizon Estimation ◽  
Estimation Model ◽  
External Interference ◽  
Estimation System ◽  
The Military

To improve the accuracy of the attitude sensor micro electro mechanical system gyroscope in low cost satellite, a nonlinear moving horizon estimation algorithm based on micro-electro mechanical system gyroscope/three-axis magnetometer is proposed in this paper. First, a quaternion micro-electro mechanical system gyroscope/three-axis magnetometer-integrated attitude estimation model is established so as to improve the accuracy of micro-electro mechanical system gyroscope. Thanks to the concealment and autonomy, these two low cost sensors have great potential in the military area. Second, taking advantage of optimal problem in coping with constraints, a real time moving horizon estimation algorithm with equality constraint is designed to deal with the disability of solving quaternion normalization analytically in the frame work of Kalman. In this algorithm, Gauss–Newton iterative method is used to obtain the optimal state estimation in the “window”. Meanwhile, strong tracking filter of arrival cost is designed outside of the “window” to enhance system robustness for that three-axis magnetometer is vulnerable to external interference. Third, the proposed MHE is applied in the micro-electro mechanical system gyroscope/three-axis magnetometer attitude estimation system. The simulation results show that the method has higher accuracy and robustness.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

Calibration of a micro-electro mechanical system-based accelerometer for vehicle navigation

2018 ◽  
Vol 233 (2) ◽  
pp. 554-560 ◽  
Author(s):  
A Ghaffari ◽  
A Khodayari ◽  
S Nosoudi ◽  
S Arefnezhad
Keyword(s):  
Mechanical System ◽  
Clustering Algorithm ◽  
Moving Objects ◽  
Inertial Sensors ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Subtractive Clustering ◽  
Measured Velocity ◽  
Preliminary Model ◽  
Dynamic Errors

Micro-electro mechanical system-based inertial sensors have broad applications in moving objects including in vehicles for navigation purposes. The low-cost micro-electro mechanical system sensors are normally subject to high dynamic errors such as linear or nonlinear bias, misalignment errors and random noises. In the class of low cost sensors, keeping the accuracy at a reasonable range has always been challenging for engineers. In this paper, a novel method for calibrating low-cost micro-electro mechanical system accelerometers is presented based on soft computing approaches. The method consists of two steps. In the first step, a preliminary model for error sources is presented based on fuzzy subtractive clustering algorithm. This model is then improved using adaptive neuro-fuzzy systems. A Kalman filter is also used to calculate the vehicle velocity and its position based on calibrated measured acceleration. The performance of the presented approach has been validated in the simulated and real experimental driving scenarios. The results show that this method can improve the accuracy of the accelerometer output, measured velocity and position of the vehicle by 79.11%, 97.63% and 99.28%, in the experimental test, respectively. The presented procedure can be used in collision avoidance and emergency brake assist systems.

A rapid seismic data calibration technique using integrated micro-electro-mechanical system inertial sensor groups for a 3C vertical seismic profile

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. P109-P118
Author(s):  
Huailiang Li ◽  
Xianguo Tuo ◽  
Tong Shen ◽  
Mark Julian Henderson ◽  
Jérémie Courtois
Keyword(s):  
Mechanical System ◽  
Seismic Data ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measuring Method ◽  
Seismic Profile ◽  
Micro Electro Mechanical System ◽  
Vertical Seismic Profile ◽  
Sensor Package

Calibration of 3C vertical seismic profile (VSP) data is an exciting challenge because the orientation of the tool is random when only seismic data are considered. We have augmented the sensor package on the VSP tool with micro-electro-mechanical system (MEMS) inertial sensors and applied a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high precision, orientation, and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass, and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field data sets and it appeared to be faster and more reliable than other approaches.

A Three-Dimensional Micro-Electro-Mechanical System (MEMS) Optical Switch Module Using Low-Cost Highly Accurate Polymer Components

2004 ◽  
Vol 43 (8B) ◽  
pp. 5824-5827 ◽  
Author(s):  
Tsuyoshi Yamamoto ◽  
Johji Yamaguchi ◽  
Nobuyuki Takeuchi ◽  
Akira Shimizu ◽  
Renshi Sawada ◽  
...  
Keyword(s):  
Mechanical System ◽  
Optical Switch ◽  
Low Cost ◽  
Three Dimensional ◽  
Micro Electro Mechanical System ◽  
Switch Module

scholarly journals Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelrahman Ali ◽  
Naser El-Sheimy
Keyword(s):  
Low Cost ◽  
Pressure Sensors ◽  
Micro Electro Mechanical System ◽  
Attitude Estimation ◽  
Handheld Devices ◽  
Accelerometer Data ◽  
Pedestrian Navigation ◽  
Heading Estimation ◽  
Sensors Fusion ◽  
New Research

The progress in the micro electro mechanical system (MEMS) sensors technology in size, cost, weight, and power consumption allows for new research opportunities in the navigation field. Today, most of smartphones, tablets, and other handheld devices are fully packed with the required sensors for any navigation system such as GPS, gyroscope, accelerometer, magnetometer, and pressure sensors. For seamless navigation, the sensors’ signal quality and the sensors availability are major challenges. Heading estimation is a fundamental challenge in the GPS-denied environments; therefore, targeting accurate attitude estimation is considered significant contribution to the overall navigation error. For that end, this research targets an improved pedestrian navigation by developing sensors fusion technique to exploit the gyroscope, magnetometer, and accelerometer data for device attitude estimation in the different environments based on quaternion mechanization. Results indicate that the improvement in the traveled distance and the heading estimations is capable of reducing the overall position error to be less than 15 m in the harsh environments.

scholarly journals Globally Asymptotic Stable Attitude Estimation with Application to MEMS Sensors

2020 ◽  
Vol 38 (3) ◽  
pp. 550-557
Author(s):  
Wei Jiang ◽  
Weiguo Zhang ◽  
Jingping Shi ◽  
Yongxi Lyu ◽  
Huakun Chen
Keyword(s):  
Low Cost ◽  
Direction Cosine ◽  
Euler Angle ◽  
Estimation Algorithm ◽  
State Observer ◽  
Attitude Estimation ◽  
The State ◽  
Sensor Data ◽  
Accurate Estimation ◽  
The Matrix

Aiming at the requirement of attitude information module with high precision, small size and low power consumption for the control of miniature UAV, a practical attitude estimation algorithm based on the micro-electro-mechanical sensor is proposed in this paper, which realizes the accurate estimation of the attitude of the UAV under the condition of low acceleration. A low-cost MEMS gyroscope, accelerometer, and magnetometer are used in the system. The Euler angle is obtained by the state observer method based on Direction Cosine Matrix (DCM) which can be got by fusing the sensor data. Firstly, based on the basic idea of TRIAD algorithm, a method to determine the attitude rotation matrix by accelerometer and magnetometric measurement is proposed. Compared with the traditional method, this method does not have to calculate the inverse of the matrix. Secondly, a state observer is intended to estimate the attitude of the system. The state observer doesn't have to observe the bias of the gyroscope, but still ensures the convergence of the Euler angle. Finally, the simulation based on the actual sampling data of the MEMS sensor shows that the output of the state observer designed in this paper still has high accuracy and good dynamic characteristics under the condition of gyroscope noise and bias.

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