139 citations · 649 across the 28 of their papers we have counts for
32 papers
Minimax-Optimal Robust Identification of Continuous-Time Systems: Handling Narrow-Band Disturbances in the Frequency Domain
Jonas Gillberg, Fredrik Gustafsson
The high-frequency spectral roll-off of continuous-time ARMA (CARMA) models can magnify the effect of narrow-band disturbances when aliasing is weak, making standard maximum-likeli…
On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model
Magnus Malmström, Isaac Skog, Daniel Axehill +1
The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the p…
Using Received Power in Microphone Arrays to Estimate Direction of Arrival
Gustav Zetterqvist, Fredrik Gustafsson, Gustaf Hendeby
Conventional direction of arrival (DOA) estimators are based on array processing using either time differences or beamforming. The proposed approach is based on the received power…
Uncertainty quantification in neural network classifiers -- a local linear approach
Magnus Malmström, Isaac Skog, Daniel Axehill +1
Classifiers based on neural networks (NN) often lack a measure of uncertainty in the predicted class. We propose a method to estimate the probability mass function (PMF) of the dif…
Track-To-Track Association for Fusion of Dimension-Reduced Estimates
Robin Forsling, Zoran Sjanic, Fredrik Gustafsson +1
Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this…
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a…
Decentralized State Estimation In A Dimension-Reduced Linear Regression
Robin Forsling, Fredrik Gustafsson, Zoran Sjanic +1
Decentralized state estimation in a communication-constrained sensor network is considered. The exchanged estimates are dimension-reduced to reduce the communication load using a l…
Learning Proposals for Practical Energy-Based Regression
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression. However, energy…
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
Taro Langner, Fredrik K. Gustafsson, Benny Avelin +3
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014. Phenotypes derived from…
Deep Energy-Based NARX Models
Johannes N. Hendriks, Fredrik K. Gustafsson, Antônio H. Ribeiro +2
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distributio…
Accurate 3D Object Detection using Energy-Based Models
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. Regressing accurate 3D bounding boxes in cluttered environments bas…
How to Train Your Energy-Based Model for Regression
Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte +1
Energy-based models (EBMs) have become increasingly popular within computer vision in recent years. While they are commonly employed for generative image modeling, recent work has…
Exploring Positive Noise in Estimation Theory
Kamiar Radnosrati, Gustaf Hendeby, Fredrik Gustafsson
Estimation of a deterministic quantity observed in non-Gaussian additive noise is explored via order statistics approach. More specifically, we study the estimation problem when me…
Energy-Based Models for Deep Probabilistic Regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat +1
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one parti…
Asynchronous Averaging of Gait Cycles for Classification of Gait and Device Modes
Parinaz Kasebzadeh, Gustaf Hendeby, Fredrik Gustafsson
An approach for computing unique gait signature using measurements collected from body-worn inertial measurement units (IMUs) is proposed. The gait signature represents one full cy…
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictio…
Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches
Di Jin, Feng Yin, Carsten Fritsche +2
We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal poster…
Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding
Johan Wahlström, Isaac Skog, Fredrik Gustafsson +2
A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio tes…
Robust Bayesian Filtering and Smoothing Using Student's t Distribution
Michael Roth, Tohid Ardeshiri, Emre Özkan +1
State estimation in heavy-tailed process and measurement noise is an important challenge that must be addressed in, e.g., tracking scenarios with agile targets and outlier-corrupte…
The Ensemble Kalman Filter: A Signal Processing Perspective
Michael Roth, Gustaf Hendeby, Carsten Fritsche +1
The ensemble Kalman filter (EnKF) is a Monte Carlo based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear and non-Gaussian state estimati…
State Estimation for Piecewise Affine State-Space Models
Rafael Rui, Tohid Ardeshiri, Henri Nurminen +2
We propose a filter for piecewise affine state-space (PWASS) models. In each filtering recursion, the true filtering posterior distribution is a mixture of truncated normal distrib…
Skew-t Filter and Smoother with Improved Covariance Matrix Approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piché +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based…
Distributed Localization of Tree-structured Scattered Sensor Networks
Sina Khoshfetrat Pakazad, Emre Özkan, Carsten Fritsche +2
Many of the distributed localization algorithms are based on relaxed optimization formulations of the localization problem. These algorithms commonly rely on first-order optimizati…
Skew-t inference with improved covariance matrix approximation
Henri Nurminen, Tohid Ardeshiri, Robert Piche +1
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our ear…