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.

scholarly journals IMU Action Recognition Based on Machine Learning

2020 ◽  
Vol 3 (6) ◽  
Author(s):  
Yongzhe Zhang ◽  
Jiachen Zheng
Keyword(s):  
Angular Velocity ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
The Public ◽  
6 Degrees Of Freedom ◽  
Three Dimensional Space

This paper adopts IMU motion recognition technology based on mechanical learning. IMU, inertial measurement unit, is a device that uses accelerometer and gyroscope to measure the three-axis attitude Angle (or angular velocity) and acceleration of an object. In a narrow sense, an IMU is equipped with gyroscope and accelerometer on three orthogonal axes, with a total of 6 degrees of freedom, to measure the angular velocity and acceleration of an object in three-dimensional space, which is known as "6-axis IMU". Broadly speaking, the IMU can add magnetometer to accelerometer and gyroscope to form the "9-axis IMU" which is now known to the public.

Optimization-based gravity-assisted calibration and axis alignment of 9-degrees of freedom inertial measurement unit without external equipment

2019 ◽  
Vol 234 (2) ◽  
pp. 192-207 ◽  
Author(s):  
Hamidreza Razavi ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Measurement Unit ◽  
Body Frame ◽  
Inertial Measurement ◽  
Calibration Methods ◽  
Axis Alignment ◽  
External Equipment ◽  
Position Calibration

Applicable in numerous fields, low-cost micro-electromechanical system inertial measurement units often require on-sight calibration by the end user due to the existence of systematic errors. A 9-degrees of freedom inertial measurement unit comprises a tri-axis accelerometer, a tri-axis gyroscope, and a tri-axis magnetometer. Various proposed multi-position calibration methods can calibrate tri-axis accelerometers and magnetometers to a degree. Yet the full calibration of a tri-axis gyroscope and axis alignment of all the sensors still often requires equipment such as a rate table to generate a priori known angular velocities and attitudes or relies on the disturbance-prone magnetometer output as a reference. This study proposes an augmentation to the popular multi-position calibration scheme, capable of fully calibrating and aligning the sensor axes of the 9-degrees of freedom inertial measurement unit while eliminating the reliance on external equipment or magnetometer. The algorithm does not rely on the inertial measurement unit attitude during various stages of the multi-position data acquisition. Instead, it uses the gravity vector measured by the accelerometer to calibrate the gyroscope and align the magnetometer axes with the sensor body frame. Experimental results using a navigation module with factory calibration and extensive simulation results indicate the current method's ability in estimating large calibration parameters with relative errors below 0.5%.

Pipeline Inspection Gauge Position Estimation Using Inertial Measurement Unit, Odometer, and a Set of Reference Stations

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Md Sheruzzaman Chowdhury ◽  
Mamoun F. Abdel-Hafez
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Position Estimation ◽  
Measurement Unit ◽  
State Estimate ◽  
Inertial Measurement ◽  
Pipeline Inspection ◽  
Global Positioning ◽  
To Receive

This paper presents a low-cost methodology to estimate the position of a pipeline inspection gauge (PIG). The environment in which the PIG navigates is inside the thick walls of a metallic pipeline, where it is not possible to receive a global positioning system (GPS) signal. As a consequence, it is necessary to use other means of navigation. A technique is presented in the paper that uses an inertial measurement unit (IMU), a speedometer, and a set of reference stations. A Kalman filter is used to fuse the measurements from the IMU, the speedometer, and the reference stations. The reference stations, with known GPS coordinates, are installed for every set interval to correct the PIG’s state estimate from the errors that accumulate due to the integration of the IMU measurements. The paper presents three scenarios. These scenarios differ in the way the update step of the Kalman filter is performed. Experimental results are presented along with a 100-run Monte Carlo test to verify the estimator’s consistency.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 2958-2961
Author(s):  
Tao Lai ◽  
Guang Long Wang ◽  
Wen Jie Zhu ◽  
Feng Qi Gao
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Angular Rate ◽  
General Structure ◽  
Test System ◽  
Measurement Unit ◽  
Storage Test ◽  
Inertial Measurement ◽  
Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

scholarly journals A Robust and Adaptive Complementary Kalman Filter Based on Mahalanobis Distance for Ultra Wideband/Inertial Measurement Unit Fusion Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3435 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Mahalanobis Distance ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Observation Error ◽  
System State ◽  
Inertial Measurement ◽  
The Impact

Ultra wideband (UWB) has been a popular technology for indoor positioning due to its high accuracy. However, in many indoor application scenarios UWB measurements are influenced by outliers under non-line of sight (NLOS) conditions. To detect and eliminate outlying UWB observations, we propose a UWB/Inertial Measurement Unit (UWB/IMU) fusion filter based on a Complementary Kalman Filter to track the errors of position, velocity and direction. By using the least squares method, the positioning residual of the UWB observation is calculated, the robustness factor of the observation is determined, and an observation weight is dynamically set. When the robustness factor does not exceed a pre-defined threshold, the observed value is considered trusted, and adaptive filtering is used to track the system state, while the abnormity of system state, which might be caused by IMU data exceptions or unreasonable noise settings, is detected by using Mahalanobis distance from the observation to the prior distribution. When the robustness factor exceeds the threshold, the observed value is considered abnormal, and robust filtering is used, whereby the impact of UWB data exceptions on the positioning results is reduced by exploiting Mahalanobis distance. Experimental results show that the observation error can be effectively estimated, and the proposed algorithm can achieve an improved positioning accuracy when affected by outlying system states of different quantity as well as outlying observations of different proportion.

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