State estimation based on improved cubature Kalman filter algorithm

Zhu, Jun; Liu, Bingchen; Wang, Haixing; Li, Zihao; Zhang, Zhe

doi:10.1049/iet-smt.2019.0363

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…However, it is not optimal in terms of mean square error [ 10 , 11 ]. The Sage–Husa adaptive filtering improves the filtering accuracy by adaptively adjusting system noise covariances [ 12 , 13 , 14 , 15 ]. However, since system noise covariances are estimated by arithmetic mean, it may not guarantee that the final filtering solution is optimal.…”

Section: Related Workmentioning

confidence: 99%

Limited Memory-Based Random-Weighted Kalman Filter

Gao,

Zong,

Zhong

et al. 2024

Sensors

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The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation.

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Section: Related Workmentioning

confidence: 99%

Limited Memory-Based Random-Weighted Kalman Filter

Gao,

Zong,

Zhong

et al. 2024

Sensors

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“…The system state residual is jointly determined by the model error and the observation error [23, 24] and can be represented by the residual.

{s}_k

represents the filter residual

{s}_k = ({Z}_k - {H}_k{X}_{k/k - 1})

at time k , and its covariance matrix is

{{\rm{w}}}_k = {H}_k{P}_{k/k - 1}{H}_k^T + {R}_k

, in which

{P}_{k/k - 1}

represents the state prediction vector covariance matrix at time k and

{R}_k

represents the observation vector covariance matrix at time k .…”

Section: Relative Navigation Filtering Algorithmmentioning

confidence: 99%

“…The system state residual is jointly determined by the model error and the observation error [23,24] and can be represented by the residual. s k represents the filter residual s k = (Z k − H k X k∕k−1 ) at time k, and its covariance matrix is w k = H k P k∕k−1 H T k + R k , in which P k∕k−1 represents the state prediction vector covariance matrix at time k and R k represents the observation vector covariance matrix at time k. The filter residuals are normalized as the following (18):…”

Section: Robust Filtering Algorithmmentioning

confidence: 99%

Unmanned ground vehicle‐unmanned aerial vehicle relative navigation robust adaptive localization algorithm

Dai

Liu

Hao

et al. 2023

IET Science Measure & Tech

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The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi-platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV-UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV-UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)-follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non-linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV-UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust-EKF and Robust-Adaptive-EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non-Gaussian distribution. The results show that under the non-Gaussian distribution conditions, the accuracy of the Robust-Adaptive-EKF algorithm is improved by about two to three times compared with the EKF and Robust-EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self-adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

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“…However, since this filter is based on a finite impulse response structure, the filtering convergence is poor [ 19 ]. The Sage–Husa noise statistic estimator has also been used to develop an adaptive KF [ 20 , 21 ]. However, the forgetting factors used in these filters are determined empirically.…”

Section: Introductionmentioning

confidence: 99%

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

Gao

Hong

et al. 2020

Sensors

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In order to achieve a highly autonomous and reliable navigation system for aerial vehicles that involves the spectral redshift navigation system (SRS), the inertial navigation (INS)/spectral redshift navigation (SRS)/celestial navigation (CNS) integrated system is designed and the spectral-redshift-based velocity measurement equation in the INS/SRS/CNS system is derived. Furthermore, a new chi-square test-based robust Kalman filter (CSTRKF) is also proposed in order to improve the robustness of the INS/SRS/CNS navigation system. In the CSTRKF, the chi-square test (CST) not only detects measurements with outliers and in non-Gaussian distributions, but also estimates the statistical characteristics of measurement noise. Finally, the results of our simulations indicate that the INS/SRS/CNS integrated navigation system with the CSTRKF possesses strong robustness and high reliability.

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State estimation based on improved cubature Kalman filter algorithm

Cited by 8 publications

References 13 publications

Limited Memory-Based Random-Weighted Kalman Filter

Limited Memory-Based Random-Weighted Kalman Filter

Unmanned ground vehicle‐unmanned aerial vehicle relative navigation robust adaptive localization algorithm

A Robust INS/SRS/CNS Integrated Navigation System with the Chi-Square Test-Based Robust Kalman Filter

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