595 citations · 595 across the 6 of their papers we have counts for
6 papers
Change-Aware Self-Adaptive AI-Aided Kalman Filters With Neural Change Point Detection
Wenyi Zhang, Xiaoyong Ni, Nir Shlezinger +1
Reliable state estimation in dynamical systems is often challenged by model mismatches, unknown noise statistics, and temporal variations. While AI-aided Kalman filters such as Kal…
BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation
Yuhan Xie, Jinhan Liu, Xiaoyong Ni +11
Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural ne…
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…
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…
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…
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…