3 citations · 4 across the 5 of their papers we have counts for
5 papers
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Outlier-Insensitive Kalman Filtering: Theory and Applications
Shunit Truzman, Guy Revach, Nir Shlezinger +1
State estimation of dynamical systems from noisy observations is a fundamental task in many applications. It is commonly addressed using the linear Kalman filter (KF), whose perfor…
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Xiaoyong Ni, Guy Revach, Nir Shlezinger
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided des…
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation
Mengyuan Zhao, Guy Revach, Tirza Routtenberg +1
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots. This paper presents…
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
Yehonatan Dahan, Guy Revach, Jindrich Dunik +1
Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep le…