Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

Hu, Gaoge; Ni, Longqiang; Gao, Bingbing; Zhu, Xinhe; Wang, Wei; Zhong, Yongmin

doi:10.1109/access.2019.2962832

Cited by 67 publications

(44 citation statements)

References 27 publications

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“…Remark 1. Suppose the dynamic system described by (10) and (11) does not involve UBB error and the linearization error is neglected, the Kalman Gain K k given in (33) can be simplified as…”

Section: Optimal Kalman Gain For Nonlinear System With Ubb Error and mentioning

confidence: 99%

“…Considering the nonlinear discrete-time dynamic system described by (10) and (11) and their equivalent equations (22) and (23), the proposed SM-HKF algorithm can be summarized as follows.…”

Section: The Sm-hkf Algorithmmentioning

confidence: 99%

“…This uncertainty is commonly characterized by stochastic errors such as the Gaussian or non-Gaussian system noise. With the assumption that the statistical characteristics of system noise are known, the issue of nonlinear system state estimation has been extensively studied [6,[8][9][10]. However, in practical applications, the system uncertainty is mixed by stochastic errors as well as unknown but bounded (UBB) errors [11].…”

Section: Introductionmentioning

confidence: 99%

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Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Zhao

Zhang

et al. 2020

Sensors

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This paper presents a new set-membership based hybrid Kalman filter (SM-HKF) by combining the Kalman filtering (KF) framework with the set-membership concept for nonlinear state estimation under systematic uncertainty consisted of both stochastic error and unknown but bounded (UBB) error. Upon the linearization of the nonlinear system model via a Taylor series expansion, this method introduces a new UBB error term by combining the linearization error with systematic UBB error through the Minkowski sum. Subsequently, an optimal Kalman gain is derived to minimize the mean squared error of the state estimate in the KF framework by taking both stochastic and UBB errors into account. The proposed SM-HKF handles the systematic UBB error, stochastic error as well as the linearization error simultaneously, thus overcoming the limitations of the extended Kalman filter (EKF). The effectiveness and superiority of the proposed SM-HKF have been verified through simulations and comparison analysis with EKF. It is shown that the SM-HKF outperforms EKF for nonlinear state estimation with systematic UBB error and stochastic error.

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“…Remark 1. Suppose the dynamic system described by (10) and (11) does not involve UBB error and the linearization error is neglected, the Kalman Gain K k given in (33) can be simplified as…”

Section: Optimal Kalman Gain For Nonlinear System With Ubb Error and mentioning

confidence: 99%

“…Considering the nonlinear discrete-time dynamic system described by (10) and (11) and their equivalent equations (22) and (23), the proposed SM-HKF algorithm can be summarized as follows.…”

Section: The Sm-hkf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Zhao

Zhang

et al. 2020

Sensors

Self Cite

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“…For hypersonic cruise vehicles (HCVs), a highly autonomous and reliable navigation system is needed [ 1 , 2 ]. The inertial navigation system (INS) is one of the most widely used navigation systems [ 3 , 4 , 5 ].…”

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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“…Navigation systems, which provide position, velocity and attitude information, are widely used in various vehicles. As single navigation system or sensor has relatively poor reliability and accuracy, integrated navigation systems are widely applied in vehicles with a high demand of reliability, such as autonomous underwater vehicles, aircrafts and rockets [1][2][3][4][5][6][7]. Each subsystem of the integrated navigation system provides its measurements to the information fusion center.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Fault Detection in Integrated Navigation Systems

Zhu

Zhou

2020

IEEE Access

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Detecting subsystem faults quickly is critical to the accuracy and reliability of integrated navigation systems. This paper, therefore, proposes an effective approach based on the novel test statistic to detect faults. Machine learning is introduced to estimate the innovation and its variance of local filter. The estimates combined with the actual ones are used to construct the test statistic, which is then proved to obey chi-square distribution. Thus fault detection can be realized by chi-square test. However, the special structure of the test statistic makes it sensitive to faults, even to the gradual faults. The experimental results demonstrate that the approach can detect faults quickly. Especially for gradual fault detection, the proposed test statistic has a marked superiority compared with the traditional test statistic of residual chi-square test.

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Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

Cited by 67 publications

References 27 publications

Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

Set-Membership Based Hybrid Kalman Filter for Nonlinear State Estimation under Systematic Uncertainty

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

A Novel Approach for Fault Detection in Integrated Navigation Systems

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