Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
IEEE Transactions on Signal Processing（2025）
Key words
Deep Learning,Augmented Kalman Filter,Bayesian Inference,Deep Neural Network,Cognitive Domains,Measurement Uncertainty,Internal Quality,Tracking Accuracy,Tracking Algorithm,Error Covariance,Bayesian Techniques,Kalman Gain,Reliable Tracking,Prior Covariance,Signal Processing,Posterior Probability,Prediction Error,Reliable Prediction,Measurement Noise,State-space Model,Extended Kalman Filter,Linear State-space Model,Model-based Algorithm,Accurate State Estimation,Evidence Lower Bound,Noise Covariance,State Transition Function,L2 Loss,Uncertainty Quantification,Inference Time
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