Inertial Navigation System Alignment Based on Fading Kalman Filter and Fixed Point Smoother

Guo, Shiluo; Xu, Jiangning; Zhang, Cheng

doi:10.1109/iscid.2017.192

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The basic idea of strong tracking filter is that we let the error caused by the uncertainty of system model or noise parameter be equivalent to the estimate error of filtering, by utilizing a suboptimal fading factor

λ_{k}

to modify the covariance matrix of one step prediction of state error

P_{k, k - 1}

, to increase the gain matrix and eventually increase the weights of the newly measured data [40–42]. According to the orthogonality principle of Wiener filter, the predicted residual has the following statistical property [43]

E [e_{k + j} e_{k}^{T}] = 0, k = 0, 1, 2, \dots, j normal= 1,2, \dots,

where

{bolde}_{k} = z_{k} - H_{k} {\overset{X}{true}}_{k | k - 1} .

…”

Section: The Multi‐sensor Integrated Navigation Methods Using Adaptiv...mentioning

confidence: 99%

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

et al. 2021

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Multi-source cooperative positioning systems relying on federated filtering have become attractive development directions of navigation strategy in real-time localization and tracking under challenging urban environment. However, local Kalman filters of traditional federated filtering may result in divergence when the system model or the measurements is inaccurate, and the fixed information distribution coefficient in federated filter cannot adaptively reflect the performance of each local filter. To improve the precision and robustness of the integrated navigation system, a novel adaptive federated strong tracking Kalman filter with dynamic fading factor mechanism for multi-sensor information fusion is proposed. Through iterative computation of the fading factor and updating the adaptive weight coefficients, strong tracking filter becomes robust to the uncertainty of system model. Meanwhile, an effective adaptive information distribution estimation algorithm based on the predicted residuals is constructed to balance the contributions of the kinematic model information and measurements on the state estimates. To ensure the stability of filtering, a simplified fusion strategy is established to solve the singular problem of the global covariance matrix of estimation error. Theoretical analysis and simulation results demonstrate the validity of the proposed approach in improving the accuracy and robustness of the integrated navigation and positioning systems. The proposed integrated multi-modal cooperative navigation and positioning algorithm will be of great significance to the implementation of real-time mobile target localization and tracking in harsh environment or dead zone. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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λ_{k}

to modify the covariance matrix of one step prediction of state error

P_{k, k - 1}

E [e_{k + j} e_{k}^{T}] = 0, k = 0, 1, 2, \dots, j normal= 1,2, \dots,

where

{bolde}_{k} = z_{k} - H_{k} {\overset{X}{true}}_{k | k - 1} .

…”

Section: The Multi‐sensor Integrated Navigation Methods Using Adaptiv...mentioning

confidence: 99%

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Up to now, many adaptive estimation algorithms [12] have been used to estimate the noise parameters in the Kalman filter process to suppress the divergence of the Kalman filter, such as innovation-based AKF [13], the expectation maximization-based AKF [14] etc. There are also many variants of Kalman filtering and fusion filtering algorithms to suppress divergence, such as Unscented Kalman Filter (UKF) [15], Extended Kalman Filter (EKF) [16], Elimination of Kalman filter [17], genetic filter algorithm, and so on.…”

Section: Introductionmentioning

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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Improved Initial Alignment Algorithm of SINS on Shaking Base Based on Kalman Filter

Shi,

Hou,

2024

Wireless Pers Commun

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Inertial Navigation System Alignment Based on Fading Kalman Filter and Fixed Point Smoother

Cited by 4 publications

References 17 publications

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

Mobile target localization and tracking techniques in harsh environment utilizing adaptive multi‐modal data fusion

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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