Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

Tseng, Chien Hao; Lin, Szu-Yin; Jwo, Dah‐Jing

doi:10.1017/s0373463316000692

Cited by 35 publications

(18 citation statements)

References 39 publications

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“…Due to the influence of lower satellite altitude angle and variability of surface materials, the multipath effect of BDS is obvious in high latitude areas, 14–16,18 which will result in measurement noise with non-Gaussian distributions. 24,25 Heavy-tailed distribution imitated by compound Gaussian distribution is one of the typical non-Gaussian distributions, and it is widely used in research works. 25–27 Therefore, we employ the heavy-tail distribution imitated by compound Gaussian distribution to study the polar alignment.…”

Section: The Bds Measurement Noise Characteristics In the Polar Regiomentioning

confidence: 99%

A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System

Zhao

Zeng

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.

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Section: The Bds Measurement Noise Characteristics In the Polar Regiomentioning

confidence: 99%

A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System

Zhao

Zeng

Liu

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…It is a robust state estimator to handle the non-Gaussian noises based on the robust estimation theory [ 28 ]. Many navigation and target problems were often handled through it under the conditions of non-Gaussian noises in the past few years [ 29 , 30 , 31 , 32 ]. Chang [ 33 ] proposed a new robust Kalman filter in recent years.…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Hou

Liu³

et al. 2018

Sensors

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Strap-down inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

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“…As a result, the estimation performance of EPF and UPF will degrade severely for such heavy-tailed non-Gaussian measurement noises. To address heavy-tailed measurement noises, many methods have been proposed, such as the Huber-based nonlinear Kalman filters (HNKFs) [ 6 , 16 , 24 , 25 , 26 , 27 , 28 ]. The HNKF is a kind of commonly used robust filter but its influence function doesn’t redescend, which results in deteriorating estimation performance [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, such Student’s t approximation may be unreasonable in some engineering applications with slightly heavy-tailed measurement noises, which may result in deteriorating filtering performance [ 30 ]. To solve this problem, a novel robust Student’s t based Kalman filter (RSTKF) has been proposed based on variational-Bayesian (VB) method [ 26 ]. In the RSTKF, the posterior PDF is approximated as a Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

Ying

Zhang

Wang

et al. 2018

Sensors

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In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN). As compared with the classic unscented particle filter (UPF) based on Gaussian assumption of measurement noise, the proposed robust particle filter based on the maximum correntropy criterion (MCC) exhibits better robustness against heavy-tailed measurement noises that are often induced by measurement outliers in CN systems. Furthermore, the proposed robust particle filter is computationally much more efficient than existing robust UPF due to the use of a Kullback-Leibler distance-resampling to adjust the number of particles online. Experimental results based on actual lake trial show that the proposed maximum correntropy based unscented particle filter (MCUPF) has better estimation accuracy than existing state-of-the-art robust filters for CN systems with heavy-tailed measurement noises, and the proposed MCUPF has lower computational complexity than existing robust particle filters.

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Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

Cited by 35 publications

References 39 publications

A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System

A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Maximum Correntropy Based Unscented Particle Filter for Cooperative Navigation with Heavy-Tailed Measurement Noises

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