595 citations · 728 across the 12 of their papers we have counts for
12 papers
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Dor H. Shmuel, Julian P. Merkofer, Guy Revach +2
Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the m…
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
Itay Buchnik, Damiano Steger, Guy Revach +3
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applic…
LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control
Solomon Goldgraber Casspi, Oliver Husser, Guy Revach +1
Stochastic control deals with finding an optimal control signal for a dynamical system in a setting with uncertainty, playing a key role in numerous applications. The linear quadra…
Outlier-Insensitive Kalman Filtering Using NUV Priors
Shunit Truzman, Guy Revach, Nir Shlezinger +1
The Kalman filter (KF) is a widely-used algorithm for tracking the latent state of a dynamical system from noisy observations. For systems that are well-described by linear Gaussia…
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
Amit Milstein, Haoran Deng, Guy Revach +2
Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies…
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising
Guy Revach, Timur Locher, Nir Shlezinger +2
Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applicati…
Anomaly Search over Composite Hypotheses in Hierarchical Statistical Models
Benjamin Wolff, Tomer Gafni, Guy Revach +2
Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum ac…
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…