Adaptive extended Kalman filtering strategies for spacecraft formation relative navigation

Fraser, Cory T.; Ulrich, Steve

doi:10.1016/j.actaastro.2020.10.016

Cited by 49 publications

(22 citation statements)

References 31 publications

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“…More recently, adaptive fuzzy Kalman was proposed for spacecraft navigation based on DOM and DOD. They claimed that fuzzy-based methods require fewer computing resources than the MLE-based method [36]. Despite this, the performance of this method has not been tested with data affected by outliers, which are common in underwater environments [37,38].…”

Section: Review Of Previous Workmentioning

confidence: 99%

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Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Shaukat

Moinuddin

Otero

2021

Sensors

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The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.

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Section: Review Of Previous Workmentioning

confidence: 99%

“…The most commonly used criteria used for fuzzy adaptive Kalman filter are DOM and DOD [35,36,52]. They are mathematically given by the following equations:…”

Section: Adaptation By Covariance Matchingmentioning

confidence: 99%

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Shaukat

Moinuddin

Otero

2021

Sensors

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“…However, even though a proper value of the noise covariance matrices is found, they remain constant reducing the robustness of the filter [ 10 , 12 ]. In some scenarios, the system can experiment a noticeable change during a particular maneuver.…”

Section: Introductionmentioning

confidence: 99%

“…For spacecraft navigation, in [ 12 ], an AKF based on ML combined with fuzzy logic is proposed. The fuzzy logic is based on the discrepancy between the estimated covariance and the theoretical covariance bounds.…”

Section: Introductionmentioning

confidence: 99%

Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

Rodríguez

Sanjurjo

Pastorino

et al. 2021

Sensors

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The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application.

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“…However, this type of algorithm has limited applicability owing to its high computational complexity. The Kalman filter (KF) [21][22][23][24][25] is yet another type of position-estimation algorithm. Kalman filter combines the state estimation of the dynamic model and the real-time measurements to generate final position estimation results.…”

Section: Introductionmentioning

confidence: 99%

Doppler‐aided Global Navigation Satellite System position estimation with B‐spline smoothing strategy

Sun

Wang

Shen

2021

IET Radar Sonar & Navi

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Smoothing process can effectively improve the accuracy of the Global Navigation Satellite System (GNSS) position estimation algorithm. In this study, a Doppler‐aided GNSS position estimation algorithm with a B‐spline smoothing strategy is proposed. B‐spline signal processing is used to smoothen the code measurements with Doppler measurements, which were selected because they are more robust and immune to cycle slips than carrier phase measurements. Next, the mathematical expressions of the smoothing process and position estimation are derived. The smoothing process is based on the structure of a digital filter, which is especially suitable for real‐time applications. The Cramer–Rao lower bound of the proposed algorithm is derived. Moreover, a complete implementation structure of the proposed algorithm is presented. The validity of the proposed algorithm is verified through scenario simulation in terms of static, kinematic, and adverse environments. The advantages of the proposed algorithm are demonstrated by comparison with existing algorithms.

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Adaptive extended Kalman filtering strategies for spacecraft formation relative navigation

Cited by 49 publications

References 31 publications

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

Doppler‐aided Global Navigation Satellite System position estimation with B‐spline smoothing strategy

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