Adaptive robust cubature Kalman filtering for satellite attitude estimation

Qiu, Zhenbing; Qian, Huaming; Wang, Guoqing

doi:10.1016/j.cja.2018.01.023

Cited by 42 publications

(12 citation statements)

References 30 publications

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“…This problem can be addressed by utilizing three-dimensional Generalized Rodrigues Parameters (GRPs) to represent the quaternion error vector and update it via quaternion multiplication. Based on this, the normalization constraint can be maintained [5,38].…”

Section: System Model Of Vehicular Ins/gnss Integrationmentioning

confidence: 99%

A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Gao

Zhu

et al. 2019

Sensors

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INS/GNSS (inertial navigation system/global navigation satellite system) integration is a promising solution of vehicle navigation for intelligent transportation systems. However, the observation of GNSS inevitably involves uncertainty due to the vulnerability to signal blockage in many urban/suburban areas, leading to the degraded navigation performance for INS/GNSS integration. This paper develops a novel robust CKF with scaling factor by combining the emerging cubature Kalman filter (CKF) with the concept of Mahalanobis distance criterion to address the above problem involved in nonlinear INS/GNSS integration. It establishes a theory of abnormal observations identification using the Mahalanobis distance criterion. Subsequently, a robust factor (scaling factor), which is calculated via the Mahalanobis distance criterion, is introduced into the standard CKF to inflate the observation noise covariance, resulting in a decreased filtering gain in the presence of abnormal observations. The proposed robust CKF can effectively resist the influence of abnormal observations on navigation solution and thus improves the robustness of CKF for vehicular INS/GNSS integration. Simulation and experimental results have demonstrated the effectiveness of the proposed robust CKF for vehicular navigation with INS/GNSS integration.

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Section: System Model Of Vehicular Ins/gnss Integrationmentioning

confidence: 99%

A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Gao

Zhu

et al. 2019

Sensors

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“…Wang et al 1 embedded the STA and the Huber technique into the UKF, which realized stable and fast tracking for an orbital vehicle when impulsive maneuver, measurement outlier, and contaminated (non-Gaussian) measurement noise occurred simultaneously. For a satellite attitude estimation system, Qiu et al 20 proposed an adaptive robust CKF where the Huber technique and multiple fading factors based on the STA were employed to suppress measurement and dynamics model errors, respectively. Since both the Huber technique and the STA sense the corresponding model error through the innovation vector, there may exist mutual interferences which are not considered.…”

Section: Introductionmentioning

confidence: 99%

“…Since both the Huber technique and the STA sense the corresponding model error through the innovation vector, there may exist mutual interferences which are not considered. 1,20 This paper is aimed at presenting a feasible state estimation method via designing a robust filter for tracking a maneuvering HGV. It flies with a Mach number greater than five and can realize long-time maneuver and fardistance glide depending on the aerodynamic lift in the near space where the altitude is between 20 km and 100 km.…”

Section: Introductionmentioning

confidence: 99%

Robust UKF-based filtering for tracking a maneuvering hypersonic glide vehicle

Huang

Zhang

Tang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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To track a non-cooperative hypersonic glide vehicle (HGV) without any precise information, an approach to the state estimation is presented based on a robust UKF-based filter (RUKFBF) in this paper. The HGV has an uncertain reentry motion because of unknown maneuvers which is a primary factor leading to degradation of tracking accuracy. Aiming at enhancing accuracy, the strong tracking algorithm (STA) is introduced to addressing the model error caused by a bank-reversal maneuver of HGV. Furthermore, the Huber technique is employed to deal with possible measurement model errors. In the RUKFBF, mutual interferences are suppressed between the STA and the Huber technique via two strategies. The one is that the calculation of the fading factor in the STA adopts an unmodified measurement noise covariance, and the other one is that two judgment criteria are proposed to limit large fading factors in the presence of measurement model errors. To simulate real tracking scenarios, the RUKFBF is tested through tracking a HGV trajectory considering a practical guidance strategy. Simulation results demonstrate the effectiveness of the RUKFBF in the presence of model errors and the observability of the estimated state.

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“…13 Second, three scalar scaling parameters are employed by the UKF, and they are always selected based on the nonlinearity of the motion or observation models. 14 In addition, the choice of parameter value will affect estimated precision of the filter. However, there is no way to accurately estimate the nonlinearity level of nonlinear model, so it is impossible to find the optimal values for these parameters.…”

Section: Introductionmentioning

confidence: 99%

Robust Square-Root Cubature FastSLAM with Genetic Operators

Havangi

2020

Robotica

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SUMMARY An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

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Adaptive robust cubature Kalman filtering for satellite attitude estimation

Cited by 42 publications

References 30 publications

A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

A Robust Cubature Kalman Filter with Abnormal Observations Identification Using the Mahalanobis Distance Criterion for Vehicular INS/GNSS Integration

Robust UKF-based filtering for tracking a maneuvering hypersonic glide vehicle

Robust Square-Root Cubature FastSLAM with Genetic Operators

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