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On sparsity by NUV-EM, Gaussian message passing, and Kalman smoothing
Normal priors with unknown variance (NUV) have long been known to promote sparsity and
to blend well with parameter learning by expectation maximization (EM). In this paper, we …
to blend well with parameter learning by expectation maximization (EM). In this paper, we …
RTSNet: Learning to smooth in partially known state-space models
The smoothing task is core to many signal-processing applications. A widely popular
smoother is the RTS algorithm, which achieves minimal mean-squared error recovery with …
smoother is the RTS algorithm, which achieves minimal mean-squared error recovery with …
Four-wheeled dead-reckoning model calibration using RTS smoothing
Localization is one of the main challenges to be addressed to develop autonomous vehicles
able to perform complex maneuvers on roads opened to public traffic. Having an accurate …
able to perform complex maneuvers on roads opened to public traffic. Having an accurate …
Model-predictive control with new NUV priors
Normals with unknown variance (NUV) can represent many useful priors including $ L_p $
norms and other sparsifying priors, and they blend well with linear-Gaussian models and …
norms and other sparsifying priors, and they blend well with linear-Gaussian models and …
[ספר][B] Control-bounded converters
H Malmberg - 2020 - research-collection.ethz.ch
The need for A/D and D/A conversion is a ubiquitous part of many of today's practical
applications. The research fields of A/D and D/A conversion are multi-disciplinary, involving …
applications. The research fields of A/D and D/A conversion are multi-disciplinary, involving …
Outlier-Insensitive Kalman Filtering: Theory and Applications
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 …
many applications. It is commonly addressed using the linear Kalman filter (KF), whose …
[ספר][B] State space methods with applications in biomedical signal processing
F Wadehn - 2019 - research-collection.ethz.ch
For more than a decade, the model-based approach to signal processing based on state
space models (SSMs) and factor graphs is being pursued at the Signal and Information …
space models (SSMs) and factor graphs is being pursued at the Signal and Information …
Outlier-insensitive Bayesian inference for linear inverse problems (OutIBI) with applications to space geodetic data
Inverse problems play a central role in data analysis across the fields of science. Many
techniques and algorithms provide parameter estimation including the best-fitting model and …
techniques and algorithms provide parameter estimation including the best-fitting model and …
Outlier-insensitive Kalman filtering using NUV priors
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 Gaussian …
system from noisy observations. For systems that are well-described by linear Gaussian …
Particle smoothing for conditionally linear Gaussian models as message passing over factor graphs
In this paper, the fixed-lag smoothing problem for conditionally linear Gaussian state-space
models is investigated from a factor graph perspective. More specifically, after formulating …
models is investigated from a factor graph perspective. More specifically, after formulating …