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.
Measuring estimator’s credibility: Noncredibility index,
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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.