Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics

Feng, Kaiqiang; Li, Jie; Zhang, Debiao; Wei, Xiaokai; Yin, Jianping

doi:10.1109/access.2020.3036423

Cited by 8 publications

(5 citation statements)

References 49 publications

(67 reference statements)

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“…Referring to [12] for the equation simplification and derivation process, the measurement update can be given by:…”

Section: Robust Kalman Filter Based On MCCmentioning

confidence: 99%

“…However, these methods still assign some weight to invalid measurements. Using a maximum correlation entropy improvement, the Kalman filter can be improved to address non-Gaussian noises [11][12][13], and the weights of measurement information are updated in real time. The Kalman filter obtains better performance when the non-Gaussian noises are suppressed.…”

Section: Introductionmentioning

confidence: 99%

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Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

Li,

Wang,

Chen

et al. 2023

Sensors

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In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.

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“…Referring to [12] for the equation simplification and derivation process, the measurement update can be given by:…”

Section: Robust Kalman Filter Based On MCCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

Li,

Wang,

Chen

et al. 2023

Sensors

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“…The SINS solution value is corrected using the estimated state feedback, the CKF filtering is implemented using the calculated variance parameter, and, finally, the forecast value is updated. The predicted value is estimated and updated, as well as the subsequent sample point during this processing [29]. The system also outputs the filtered navigation parameter data at the same time.…”

Section: Algorithm Test Planmentioning

confidence: 99%

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Liu,

Di,

Peng

et al. 2023

Sustainability

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Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

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“…Chen proposed an adaptive extended Kalman filter (EKF) algorithm in the literature [19] to eliminate the improper selection of noise covariance, and experiments show that the algorithm effectively improves the positioning accuracy of INS/GNSS. Feng proposed a new robust volume Kalman filter based on adaptive information entropy theory in the literature [20], which effectively suppresses the process uncertainty and non-Gaussian measurement noise, and experiments show that the filter has higher estimation accuracy and stronger robustness. Sun proposed a new measurement noise covariance update scheme in the literature [21], which was tested on a integrated GNSS/inertial measurement unit (IMU) navigation system in urban areas using adaptive metrics generated from pseudorange error prediction results, and the experiments showed that the accuracy of 3D positioning was effectively improved.…”

Section: Introductionmentioning

confidence: 99%

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Wang

Cheng

et al. 2023

Meas. Sci. Technol.

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Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy.In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

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Robust Cubature Kalman Filter for SINS/GPS Integrated Navigation Systems With Unknown Noise Statistics

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 8 publications

References 49 publications

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

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