Error Distribution Method and Analysis of Observability Degree Based on the Covariances in Kalman Filter

Yan, Gongmin; Yang, Xiaokang; Su, Xingjun; Weng, Jun; Yong-yuan, Qin

doi:10.23919/chicc.2018.8482583

Cited by 14 publications

(5 citation statements)

References 3 publications

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“…The observability calculation method based on covariance analysis was defined as follows [ 22 ]:

where

represents the value corresponding to a certain state component in the diagonal element of the initial mean square error matrix

of the Kalman filter, and the initial mean square error matrix is usually set according to the empirical value.

represents the value corresponding to a certain state component in the diagonal element of the Kalman filter mean square error matrix

at a certain time.…”

Section: System-level Calibration Based On 30-dimensional Kalman Filtermentioning

confidence: 99%

An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

Wei

Zha

et al. 2022

Sensors

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The system-level calibration technology of rotational inertial navigation is one of the main methods to improve the accuracy of inertial navigation, and the design of the calibration scheme is the key to calibration technology. By the establishment of the error model of inertial navigation system, a 30-position calibration scheme is designed in this study. Based on the 30-dimensional Kalman filter, the constant errors, scale factor errors and installation error of gyroscope and accelerometer are identified. Comparing the traditional schemes and the 30-position scheme with the simulation experiment, the observability of the 30-position scheme is higher, the residual error of the estimated sensor is smaller and the navigation positioning accuracy after the estimated inertial sensor error parameter compensation is higher, which verifies the feasibility of the 30-position scheme. Finally, the measured experiment uses the 30-position scheme to estimate the error of a certain type of IMU sensor, and the calibration curve of the error parameter is well converged before the end of the calibration experiment, so it has certain practical value.

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“…The observability calculation method based on covariance analysis was defined as follows [ 22 ]:

where

represents the value corresponding to a certain state component in the diagonal element of the initial mean square error matrix

of the Kalman filter, and the initial mean square error matrix is usually set according to the empirical value.

represents the value corresponding to a certain state component in the diagonal element of the Kalman filter mean square error matrix

at a certain time.…”

Section: System-level Calibration Based On 30-dimensional Kalman Filtermentioning

confidence: 99%

An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

Wei

Zha

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The system model in this paper is based on Yan Gongmin's simplified linear SINS error model [18], and the model is described as follows:…”

Section: Fig 1 Flow Chart Of Sinsmentioning

confidence: 99%

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Shi

Hou

2022

Preprint

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In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars introduce various nonlinear filtering methods to solve this problem, but these filtering methods not only have large amount of calculation, but also are low accuracy. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. The experimental results show that the improved algorithm has high anti-interference and anti-divergence ability. Under the simulation conditions, pitch, roll and heading errors can be stabilized at about 10 degrees. The real measurement with Redmi K30Pro mobile phone shows that the alignment result of the improved algorithm is consistent, and the difference of multiple alignment results is stable within 5 degrees, while the difference of traditional method is more than 20 degrees. It can be applied in smart phone navigation with the strapdown inertial navigation system.

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“…From the real filtered innovation time series, the following variance matrix is estimated (19): (18) and ( 17) should be consistent, therefore…”

Section: Combining Sequential Inertial Filtering To Improve Adaptive ...mentioning

confidence: 99%

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Shi

Hou

Shi

et al. 2022

2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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Error Distribution Method and Analysis of Observability Degree Based on the Covariances in Kalman Filter

Cited by 14 publications

References 3 publications

An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

An Improved System-Level Calibration Scheme for Rotational Inertial Navigation Systems

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

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