Improving measurement quality of a MEMS-based gyro-free inertial navigation system

Naseri, H.; Homaeinezhad, Mohammad Reza

doi:10.1016/j.sna.2013.12.011

Cited by 48 publications

(23 citation statements)

References 26 publications

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“…If white noise is added to the continuous model of a system, a randomwalks noise appears in the corresponding discrete model of system. This behavior is coincident with the noise model of real applications [30][31][32].…”

Section: Establishing Parameter-regressor Model For Estimating Paramesupporting

confidence: 79%

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

Homaeinezhad¹,

Moghaddam²,

Khakpour³

et al. 2015

Asian Journal of Control

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The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.

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Section: Establishing Parameter-regressor Model For Estimating Paramesupporting

confidence: 79%

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

Homaeinezhad¹,

Moghaddam²,

Khakpour³

et al. 2015

Asian Journal of Control

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“…In this study, an AHRS consisting of 5 MPU6050/GY87 modules, each containing 1 triaxial accelerometer, gyroscope, and magnetometer, is designed as shown in Figure . Modules are mounted on the faces of a frustum.…”

Section: Experimental Results and Further Discussionmentioning

confidence: 99%

“…In this study, an AHRS consisting of 5 MPU6050/GY87 modules, each containing 1 triaxial accelerometer, gyroscope, and magnetometer, is designed as shown in Figure 5. 29 Modules are mounted on the faces of a frustum. After rotating each module's frame to the AHRS body frame, the ensemble average of modules' data in the direction of each axis is calculated.…”

Section: Arrays Of Accelerometers Gyros and Magnetometersmentioning

confidence: 99%

Attitude determination by combining arrays of MEMS accelerometers, gyros, and magnetometers via quaternion‐based complementary filter

Ghasemi-Moghadam

Homaeinezhad

2017

Int J Numerical Modelling

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In this study, an Attitude and Heading Reference System (AHRS) consisting of 5 modules is designed where each module has a triaxial gyroscope, accelerometer, and magnetometer. First, a method based on the Levenberg‐Marquardt algorithm (LMA) is utilized to correct the bias error, scale factor and axes nonorthogonality. Also, the data from the 5 modules of AHRS are ensemble averaged to reduce the adverse effects of high‐frequency noises. Then, the obtained trends are used in an orientation estimation algorithm based on a complementary filter algorithm. In this algorithm, the dynamical accelerations are first decreased via a low‐pass filter. Afterwards, it is determined using an algorithm whether the system is experiencing magnetic distortion or not. If distortion is verified, magnetometers' data are discarded, as it will introduce noticeable error in estimating heading angle. In this case, the heading angle starts to diverge; however, employing 5 modules in the system decreases divergence rate noticeably, such that after 2 minutes in quasi‐static conditions, the pitch, roll, and heading angles' errors decrease, respectively, from 36.195°, 23.201°, and 12.541° when only 1 module is used to 2.511°, 3.972°, and 0.984° when all 5 modules are used. Moreover, in dynamical conditions, these errors decrease from 37.916°, 13.633°, and 13.071° to 6.514°, 5.961°, and 0.284°. Once the distortion is removed from magnetic field, the magnetometers' data are used again to correct the heading error. The obtained results show that the root mean square (RMS) errors of pitch, roll, and heading angles in quasi‐static conditions are 0.536°, 0.323°, and 0.601°, whereas in dynamical condition, they are 1.267°, 1.535°, and 0.994°, respectively.

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“…Without loss of generality, the data sampling interval of accelerometers is assumed to be 0.01 s. The update frequency of the UKF coincides with the accelerometer sampling frequency. The reference trajectories of f and ω for simulations are 2 2sin(2 ) 4sin(2 ) m/s 8sin (2 ) x y…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…An inertial measurement unit (IMU) is a multisensor system which can be used for motion detection and selfnavigation [1][2][3][4]. Conventionally, an IMU is composed of gyroscopes measuring the body angular velocity and accelerometers measuring the body translational acceleration.…”

Section: Introductionmentioning

confidence: 99%

An effective unscented Kalman filter for state estimation of a gyro-free inertial measurement unit

Liu

Zhang

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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This study reports a gyro-free inertial measurement unit (IMU) using solely four triaxial accelerometers. System equations and a configuration which is feasible for the gyro-free IMU design are presented. The propagation of accelerometer measurement errors is analyzed. An unscented Kalman filter (UKF) is proposed for state estimation. Simulation results show that the system state is robustly estimated by the proposed UKF. Furthermore, compared with the results of error analysis, the UKF provides effective error reductions on state estimation. The error of angular velocity estimation over full scale (FS) is about ±0.4%FS.

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Improving measurement quality of a MEMS-based gyro-free inertial navigation system

Cited by 48 publications

References 26 publications

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

Attitude determination by combining arrays of MEMS accelerometers, gyros, and magnetometers via quaternion‐based complementary filter

An effective unscented Kalman filter for state estimation of a gyro-free inertial measurement unit

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