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20172021
most citedLearning to solve inverse problems using Wasserstein loss

25 citations · 42 across the 9 of their papers we have counts for

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5 papers · 1 filter

eess.SP2021

Quantifying and Computing Covariance Uncertainty

Filip Elvander, Johan Karlsson, Toon van Waterschoot

In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal. Specifically, for…

eess.SP20211 cited

Mixed-Spectrum Signals -- Discrete Approximations and Variance Expressions for Covariance Estimates

Filip Elvander, Johan Karlsson

The estimation of the covariance function of a stochastic process, or signal, is of integral importance for a multitude of signal processing applications. In this work, we derive c…

eess.SP2019

Fusion of Sensors Data in Automotive Radar Systems: A Spectral Estimation Approach

Bin Zhu, Augusto Ferrante, Johan Karlsson +1

To accurately estimate locations and velocities of surrounding targets (cars) is crucial for advanced driver assistance systems based on radar sensors. In this paper we derive meth…

eess.SP20193 cited

Multi-Marginal Optimal Mass Transport with Partial Information

Filip Elvander, Isabel Haasler, Andreas Jakobsson +1

During recent decades, there has been a substantial development in optimal mass transport theory and methods. In this work, we consider multi-marginal problems wherein only partial…

eess.SP2018

Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport

Filip Elvander, Isabel Haasler, Andreas Jakobsson +1

This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of…