Sensor node calibration in presence of a dominant reflective plane

Tegler, Erik; Larsson, Martin; Oskarsson, Magnus; Åström, Kalle (2022). Sensor node calibration in presence of a dominant reflective plane 30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings, 2022-August,, 1941 - 1945. 30th European Signal Processing Conference, EUSIPCO 2022. Belgrade, Serbia: European Signal Processing Conference, EUSIPCO
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Conference Proceeding/Paper | Published | English
Authors:
Tegler, Erik ; Larsson, Martin ; Oskarsson, Magnus ; Åström, Kalle
Department:
Mathematics (Faculty of Engineering)
LTH Profile Area: AI and Digitalization
eSSENCE: The e-Science Collaboration
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
Mathematical Imaging Group
LTH Profile Area: Engineering Health
Stroke Imaging Research group
Research Group:
Mathematical Imaging Group
Stroke Imaging Research group
Abstract:

Recent advances in simultaneous estimation of both receiver and sender positions in ad-hoc sensor networks have made it possible to automatically calibrate node positions - a prerequisite for many applications. In man-made environments there are often large planar reflective surfaces that give significant reverberations. In this paper, we study geometric problems of receiver-sender node calibration in the presence of such reflective planes. We establish a rank-1 factorization problem that can be used to simplify the estimation. We also show how to estimate offsets, in the Time difference of arrival case, using only the rank constraint. Finally, we present a new solver for the minimal cases of sender-receiver position estimation. These contributions result in a powerful stratified approach for the node calibration problem, given a reflective plane. The methods are verified with both synthetic and real data.

Keywords:
minimal problems ; reverberations ; self-calibration ; TDOA ; TOA
ISBN:
9789082797091
ISSN:
2219-5491
LUP-ID:
b40a69b1-7965-4fa3-81a4-7099774ff28b | Link: https://lup.lub.lu.se/record/b40a69b1-7965-4fa3-81a4-7099774ff28b | Statistics

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