Accuracy improvement of a multi-MEMS inertial measurement unit by using an iterative UFIR filter

Rasoulzadeh, Raouf; Shahri, Alireza Mohammad

doi:10.1109/euronav.2017.7954219

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…One of the later publications reviewed, published by Rasoulzdeh and Shahri in 2017, states that the vast majority of research in the area of MEMS sensor arrays is Kalman filter-based [21]. They then present an algorithm that is two stage.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Milam¹

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Most small unmanned aerial systems in use today, employ extended Kalman filter sensor fusion algorithms in order to provide accurate estimations of attitude or orientation. These complex algorithms use measurements from GPS receivers and magnetometer sensors that can be rendered useless in GPS denied environments or areas of significant magnetic interference, such as inside buildings or other structures. The complexity of these algorithms makes them inaccessible for some researchers and hobbyists who wish to code their own attitude estimation algorithms. This complexity is also computationally expensive and requires processors that are powerful enough to operate the algorithms along with any command and control functions required by the application. In contrast, there are simple sensor fusion algorithms such as the complementary filter or linear Kalman filter, that are commonly used by hobbyists because they are relatively easy to implement and computationally lightweight. However, these methods are not as accurate as the extended Kalman filter and therefore, are not adequate for some of the emerging precision applications in aerial robotics. The goal of this research is to investigate an attitude estimation algorithm that uses two separate inertial measurement units (IMUs), each consisting of tri-axis accelerometers and tri-axis gyroscopes. This dual or twin IMU (TIMU) algorithm is compared to several common algorithms that only use one IMU, such as the complementary filter and linear Kalman filter. Analysis of a one degree of freedom experiment shows that the TIMU algorithm provides a more accurate attitude estimate. The analysis also shows that distance between the IMU and the rotating body's center of gravity can have an inverse effect on attitude accuracy. The ability of the algorithms to provide an accurate estimate of the rate of attitude change is used as a performance metric, in addition to the accuracy of attitude estimates. The complexity of the twin IMU algorithm is kept to a minimum. It is presented in a way that can be easily programed by the layman and has a small computational footprint.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Milam¹

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“…In its implementation, motion is divided into two, namely straight motion and rotational motion. The angular velocity and acceleration are obtained by decreasing the angle with respect to time, because the filtered output of the IMU sensor is an angle [15], where x is the x angle, wΦx is the angular velocity x, and aΦx is the angular acceleration x , as shown in Table 3. Based on Table 3, the test was carried out by carrying out activities, normal, falling forward, backward, to the right and falling to the left 5 times.…”

Section: Measurement and Testing Of System Performancementioning

confidence: 99%

Implementation of IMU sensor for Fall Detection in Dementia Syndrome Patients with Location Notification System

Yulastri¹,

Madona²,

Ulfa³

et al. 2022

J. Phys.: Conf. Ser.

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Monitoring tool for patients at risk of dementia has been made using an IMU sensor with Location Notification System. The aim of this research is to design and manufacture a tool using the IMU sensor to identify and determine the characteristics of a person’s condition in falling for patients at risk of dementia. The research method starts from making prototypes, and measuring system performance. The results of the measurement of system performance are shown by the IMU sensor readings with an average x angle value of 175.74, angular velocity x, and angular acceleration x an average of 175.70 and 0.052 for forward fall activities. For activities falling backwards the average x angle value is 27.97, the angular velocity x, and the angular acceleration x averages 28.12 and -0.25. For activities falling to the right the average x angle value is 85.07, the angular velocity x, and the angular acceleration x averages 37.31 and 79.59. As for the activity falling to the left, the average x angle value is 32.91, the angular velocity x, and the angular acceleration x averages 32.54 and 0.62. Overall the tool can function well. Suggestions for further research, methods can be added to predict the position of the nearest hospital and police station based on the Internet of Things.

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“…For the ℎ sensor subsystem, filtering error [ ] is written as (24) where filtering gain [ ] is specified as (27). So the cross covariance matrix between the ℎ and the ℎ subsystem can be showed as…”

Section: Cross Covariance Matrices and Iterative Computationmentioning

confidence: 99%

“…In addition, there are a few new study and improved solutions on FIR filtering [20][21][22] developed nowadays. Some practical applications were reported [23,24] as well.…”

Section: Introductionmentioning

confidence: 99%

State Fusion of Decentralized Optimal Unbiased FIR Filters

Fan

Liu

2018

Journal of Electrical and Computer Engineering

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The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.

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Accuracy improvement of a multi-MEMS inertial measurement unit by using an iterative UFIR filter

Cited by 5 publications

References 20 publications

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Implementation of IMU sensor for Fall Detection in Dementia Syndrome Patients with Location Notification System

State Fusion of Decentralized Optimal Unbiased FIR Filters

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