Sammanfattning

In this paper we revisit the Time-of-Arrival self-calibration problem. In particular we focus on imbalanced problem instances where there are significantly more sources compared to the number of receivers, which is a common configuration in real applications. Using an implicit representation, we are able to re-parameterize the sensor node self-calibration problem using only the parameters of the receiver positions. Making the source positions implicit, we show that it is possible to linearize the maximum-likelihood error around the measured distances, resulting in a Sampson-like approximation. Given four unknown receiver positions and a large number of unknown sender positions, we show that our formulation leads to algorithms for robust calibration, with significant speed-up compared to running the full optimization over all unknowns. The proposed method is tested on both synthetic and real data.

Originalspråkengelska
Titel på värdpublikation31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
FörlagEuropean Signal Processing Conference, EUSIPCO
Sidor1644-1648
Antal sidor5
ISBN (elektroniskt)9789464593600
DOI
StatusPublished - 2023
Evenemang31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland
Varaktighet: 2023 sep. 42023 sep. 8

Publikationsserier

NamnEuropean Signal Processing Conference
ISSN (tryckt)2219-5491

Konferens

Konferens31st European Signal Processing Conference, EUSIPCO 2023
Land/TerritoriumFinland
OrtHelsinki
Period2023/09/042023/09/08

Ämnesklassifikation (UKÄ)

  • Signalbehandling

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