A redundant fused MIMU attitude system algorithm based on two-stage data fusion of MEMS gyro clusters array

Xue, Liang; Bo, Yang; Yang, Xiaogang; Yuan, Ding; Wang, Xinguo; Chang, Honglong

doi:10.1016/j.measurement.2021.109993

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Equation ( 19) is the iterative formula of Newton algorithm [34,35]. Still, the algorithm has strict requirements on the function, and the computational complexity is high because the optimization direction does not necessarily decrease towards the direction of the function value.…”

Section: Ellipsoid Fitting Based On Lm-rlsmentioning

confidence: 99%

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Jiang,

Tan

2024

Meas. Sci. Technol.

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Aiming at the problems that the traditional single ellipsoid fitting fails to eliminate the errors of the geomagnetic sensor and does not consider the geomagnetic anomaly information, etc., this paper proposes a Robust Least Squares combined with the Levenberg-Marquardt (LM-RLS) algorithm to fit an accurate ellipsoid to achieve accurate data calibration. According to the geomagnetic sensor error model, the total amount of geomagnetism is used as the constraint, the fitted ellipsoid coefficients of Robust Least Squares (RLS) are used as the initial values, and the LM algorithm is used to calculate the magnetic field dispersion points to the nearest point of the fitted ellipsoid surface. Then, the initial calibration value is solved by the criterion of the minimum weighted sum of squares of the closest point distance. Finally, the accurate ellipsoid is obtained to realize data calibration. The results of the dynamic experiment show that after the ellipsoid is precisely fitted by the LM-RLS algorithm, the sum of the absolute values of the distance from the magnetic field discrete data to the ellipsoid surface is reduced by 48.33% (RLS) and 43.10% (RWTLS). The relative errors of the magnetic field in the X, Y, and Z axes are reduced from 0.48%, 0.78%, 1.48% (RLS) and 0.30%, 0.58%, 1.14% (RWTLS) to 0.10%, 0.04%, 0.16 %, respectively. The accuracy of geomagnetic calibration is improved by 68.06% (RLS) and 61.02% (RWTLS) in the dynamic experiment. The algorithm improves the accuracy of geomagnetic measurement, which provides a new idea for the calibration of three-axis geomagnetic sensors.

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Section: Ellipsoid Fitting Based On Lm-rlsmentioning

confidence: 99%

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Jiang,

Tan

2024

Meas. Sci. Technol.

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“…The covariance matrix of the vectors W(t) and V(t) are Q and R, respectively. The matrices Q and R are not necessarily diagonal because of the gyro's noise correlation; thus, the matrices Q, R and q are given in Equation (5).…”

Section: Modeling Of a Fused-gyro-array Kf Algorithmmentioning

confidence: 99%

“…However, due to the limitation of the working principle of the MEMS inertial device, the MIMU's measurement noise is large, and the parameters are also unstable, which makes it difficult to provide inertial signals with low drift error for the MIMU composed of three independent gyroscopes and accelerometers. The technology of multiple signals fusion provides a new method for reducing the MIMU's drift error and improving its accuracy [4][5][6]. Compared to an MIMU constructed of three orthogonal sensors and a sensor array (Figure 1a,b), using a non-orthogonal array to design a MIMU can increase its measurement information on the sensors' coordinate frame, which is mapped from the input rate and acceleration of the MIMU.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, an integrated navigation algorithm for combining multiple MIMU and GPS was designed in [17], in which the three-dimensional trajectory dynamics model was used as the system model to design a centralized KF and estimate navigation parameters. In addition, a planar MIMU with 8 × 8 array was formed by 64 chips, and six positions and angular rate position method were used to calibrate the errors and installation angles of gyroscopes and accelerometers in MIMUs [18]; thus, the dynamic navigation performance The technology of multiple signals fusion provides a new method for reducing the MIMU's drift error and improving its accuracy [4][5][6]. Compared to an MIMU constructed of three orthogonal sensors and a sensor array (Figure 1a,b), using a non-orthogonal array to design a MIMU can increase its measurement information on the sensors' coordinate frame, which is mapped from the input rate and acceleration of the MIMU.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array

Xue¹,

Bo²,

Wang³

et al. 2023

Micromachines

Self Cite

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A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU’s accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU’s performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro’s error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro’s ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

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“…They also used Allan variance analysis to evaluate the error characteristics of the array when in a stationary state [19]. In addition, various arrays have been designed by researchers to quantitatively analyze static noise performance [20,21]. The results indicated a significant improvement in the static noise performance of the arrays.…”

Section: Introductionmentioning

confidence: 99%

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Cao,

Luo,

Liu

et al. 2024

Sensors

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In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

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A redundant fused MIMU attitude system algorithm based on two-stage data fusion of MEMS gyro clusters array

Cited by 11 publications

References 20 publications

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

Improved ellipsoid fitting aided geomagnetic sensor calibration algorithm

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

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