Hybrid PGM filtering method for short- and long-term cislunar target tracking with angles-only data and prior information fusion.
Kernel-based ensemble Gaussian mixture filtering for orbit determination with sparse data
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Reinforcement learning is used to learn adaptive policies for selecting parameters in nonlinear Bayesian filters, improving estimate quality and consistency in experiments with the unscented Kalman filter and stochastic integration filter.
citing papers explorer
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Hybrid Particle Gaussian Mixture (H-PGM) Solution for Cislunar Target Tracking
Hybrid PGM filtering method for short- and long-term cislunar target tracking with angles-only data and prior information fusion.
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Learning Adaptive Parameter Policies for Nonlinear Bayesian Filtering
Reinforcement learning is used to learn adaptive policies for selecting parameters in nonlinear Bayesian filters, improving estimate quality and consistency in experiments with the unscented Kalman filter and stochastic integration filter.