Publications de Miranda Krekovic

Point, Plane, Set, Match! Winning the Echo Grand SLAM

Some of the most important and challenging problems in science are inverse problems. They allow us to understand phenomena that cannot be measured directly. Inverse problems might not always have a unique or stable solution, or might not have any solution ...

EPFL2020

Coordinate Difference Matrices

Martin Vetterli, Gilles Baechler, Golnooshsadat Elhami, Miranda Krekovic

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called ...

SIAM PUBLICATIONS2020

Shapes From Echoes: Uniqueness From Point-to-Plane Distance Matrices

Martin Vetterli, Ivan Dokmanic, Miranda Krekovic

We study the problem of localizing a configuration of points and planes from the collection of point-to-plane distances. This problem models simultaneous localization and mapping from acoustic echoes as well as the & x201C;structure from sound & x201D; app ...

2020

Super Resolution Phase Retrieval for Sparse Signals

Martin Vetterli, Amina Chebira, Gilles Baechler, Juri Ranieri, Miranda Krekovic

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Structure from sound with incomplete data

Martin Vetterli, Gilles Baechler, Ivan Dokmanic, Miranda Krekovic

In this paper, we consider the problem of jointly localizing a microphone array and identifying the direction of arrival of acoustic events. Under the assumption that the sources are in the far field, this problem can be formulated as a constrained low-ran ...

2018

Combining Range and Direction for Improved Localization

Martin Vetterli, Robin Scheibler, Gilles Baechler, Frederike Dümbgen, Golnooshsadat Elhami, Miranda Krekovic, Adam James Scholefield

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use ...

2018

Omnidirectional bats, point-to-plane distances, and the price of uniqueness

Martin Vetterli, Ivan Dokmanic, Miranda Krekovic

We study simultaneous localization and mapping with a device that uses reflections to measure its distance from walls. Such a device can be realized acoustically with a synchronized collocated source and receiver; it behaves like a bat with no capacity for ...

Ieee2017