595 citations · 728 across the 12 of their papers we have counts for
5 papers · 1 filter
Unsupervised Learned Kalman Filtering
Guy Revach, Nir Shlezinger, Timur Locher +3
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF…
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Itzik Klein, Guy Revach, Nir Shlezinger +3
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a…
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
Guy Revach, Xiaoyong Ni, Nir Shlezinger +2
The smoothing task is core to many signal processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error r…
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
Julian P. Merkofer, Guy Revach, Nir Shlezinger +2
Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classifi…
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni +3
State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (S…