Abstract

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.

Original languageEnglish
Title of host publication31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1644-1648
Number of pages5
ISBN (Electronic)9789464593600
DOIs
Publication statusPublished - 2023
Event31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland
Duration: 2023 Sept 42023 Sept 8

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference31st European Signal Processing Conference, EUSIPCO 2023
Country/TerritoryFinland
CityHelsinki
Period2023/09/042023/09/08

Subject classification (UKÄ)

  • Signal Processing

Free keywords

  • robust optimization
  • Sensor node calibration
  • Time-of-Arrival

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