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Found 15 papers, 14 papers with code
Outlier-Insensitive Kalman Filtering: Theory and Applications
State estimation of dynamical systems from noisy observations is a fundamental task in many applications.
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models.
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots.
Uncertainty Quantification in Deep Learning Based Kalman Filters
Various algorithms combine deep neural networks (DNNs) and Kalman filters (KFs) to learn from data to track in complex dynamics.
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Direction of arrival (DoA) estimation is a fundamental task in array processing.
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach.
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising
This paper introduces HKF, a hierarchical and adaptive Kalman filter, which uses a proprietary state space model to effectively capture both intra- and inter-heartbeat dynamics for ECG signal denoising.
LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control
The linear quadratic Gaussian (LQG) is a widely-used setting, where the system dynamics is represented as a linear Gaussian statespace (SS) model, and the objective function is quadratic.
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading.
Outlier-Insensitive Kalman Filtering Using NUV Priors
The former was previously proposed for the task of smoothing with outliers and was adapted here to filtering, while both EM and AM obtained the same performance and outperformed the other algorithms, the AM approach is less complex and thus requires 40 percentage less run-time.
Unsupervised Learned Kalman Filtering
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), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
The smoothing task is core to many signal processing applications.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system.
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
State estimation of dynamical systems in real-time is a fundamental task in signal processing.
