Dual-Satellite Geolocation With Ephemeris Correction and Uncertainty Mapping

Geeraert, J.; McMahon, Jay W.

doi:10.1109/taes.2019.2920045

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…We next find the approximate posterior of the state vector x k and biases in b, q(x k , b), using the obtained posterior of the precision matrices Λ i , q(Λ i ), given in (20). Again, evaluating the integral in (14) and discarding the terms that are not dependent on x k or b, simplify the ELBO into 21) where γ i,k is equal to, after applying (16),…”

Section: Outlier-robust Skfsmentioning

confidence: 99%

“…The SKF has attracted a significant amount of attention due to its wide applications in e.g., orbit determination [12], Mars entry navigation [13] and source geolocation when satellite ephemeris errors are present [14]. In [15], it was established theoretically that the SKF is an unbiased state estimator under linear Gaussian state-space models.…”

Section: Introductionmentioning

confidence: 99%

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Outlier-Robust Schmidt-Kalman Filter Using Variational Inference

Liu

Xue³

et al. 2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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The Schmidt-Kalman filter (SKF) achieves filtering consistency in the presence of biases in system dynamic and measurement models through accounting for their impacts when updating the state estimate and covariance. However, the performance of the SKF may break down when the measurements are subject to non-Gaussian and heavy-tail noise. To address this, we impose the Wishart prior distribution on the precision matrix of measurement noise, such that the measurement likelihood now has heavier tails than the Gaussian distribution to deal with the potential occurrence of outliers. Variational inference is invoked to establish analytically tractable methods for computing the posterior of the system state, system biases, and the measurement noise precision matrix. The principle ofn the SKF considers the effect of system biases but does not actively estimate them when two variants of outlier-robust SKFs are incorporated. We evaluate their performance in terms of estimation accuracy and filtering consistency using simulations and real-world data. Promising results are obtained.

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Section: Outlier-robust Skfsmentioning

confidence: 99%