A New Robust Centered Error Entropy Cubature Kalman Filter

Yang, Baojian; Cao, Lu; Li, Lingwei; Jiang, Chung-Hsiang; Ran, Dechao; Xiao, Bing

doi:10.1109/iccsse52761.2021.9545184

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…where ω oi = ½0-ω 0 0 is the orbital angular velocity and A bo is the conversion matrix from the orbit frame to the satellite body frame [46] and is expressed as…”

Section: Simulationmentioning

confidence: 99%

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

Yang

Huang²,

Cao³

2022

Space Sci Technol

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The classical sigma-point Kalman filter (SPKF) is widely used in a spacecraft state estimation area with the Gaussian white noise hypothesis. The actual sensor noise is often disturbed by outliers in the harsh space environment, and the SPKF algorithm will reduce the filtering accuracy or even diverge. In this study, to enhance the robustness under non-Gaussian noise condition, the outlier-robust SPKF algorithm based on a centered error entropy (CEE) criterion is derived. Unscented Kalman filter (UKF) is typical of SPKF; combining the deterministic sampling criterion with the centered error entropy criterion, a robust centered error entropy UKF (CEEUKF) algorithm is proposed. The CEEUKF uses the unscented transformation (UT) method to perform time update step and then uses the robust regression model and CEE criterion to reconstruct the measurement update step. The effectiveness of the proposed CEEUKF is verified by a spacecraft attitude determination system.

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“…where ω oi = ½0-ω 0 0 is the orbital angular velocity and A bo is the conversion matrix from the orbit frame to the satellite body frame [46] and is expressed as…”

Section: Simulationmentioning

confidence: 99%

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

Yang

Huang²,

Cao³

2022

Space Sci Technol

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New robust state‐estimation frameworks for angles‐only underwater target tracking problems

Urooj,

Radhakrishnan

2023

Adaptive Control & Signal

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SummaryTracking a moving target using only angle measurements is a challenging and complex problem. This is because the measurements received from a sensor on a moving observer provide very little information about the target's movements. Additionally, there are uncertainties regarding the target's initial range and speed, which can further complicate the estimation process. The angle measurements themselves carry a lot of uncertainties as they can be affected by significant outliers, which do not fit the standard Gaussian distribution that estimation algorithms typically assume. Furthermore, the observer's position can also be uncertain due to various environmental factors. To address these challenges and to propose a robust, unified and accurate solution, two new estimation frameworks are proposed. They involve making use of a range and speed parametrization approach to compensate for the initial uncertainty in the target's range and speed. They also involve using a maximum correntropy (MC) criterion and a numerically stable centered error entropy (CEE) criterion to deal with measurement outliers and the observer motion uncertainties. The performance of the proposed frameworks is evaluated by comparing them to the traditional unscented Kalman filter (UKF) and its parametrized versions using different performance metrics. The simulations conducted showed that the developed algorithms perform better in terms of estimation accuracy than conventional methods. Specifically, the second framework based on the CEE criterion outperformed MC‐based estimators.

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Centered Error Entropy Based Central Difference Kalman Filter for Nanosatellite Attitude Estimation

Yang¹,

Huang²,

Cao³

2022

Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)

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A New Robust Centered Error Entropy Cubature Kalman Filter

Cited by 3 publications

References 17 publications

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

New robust state‐estimation frameworks for angles‐only underwater target tracking problems

Centered Error Entropy Based Central Difference Kalman Filter for Nanosatellite Attitude Estimation

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