Cooperative Heading Estimation with von Mises-Fisher Distribution and Particle Filtering

Mäkelä, Maija; Kirkko-Jaakkola, Martti; Hammarberg, Toni; Malkamäki, Tuomo; Rantanen, Jesperi; Kaasalainen, Sanna

doi:10.23919/fusion49751.2022.9841227

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Infrastructure-free Cooperative Positioning1

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2024

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“…The state is modeled as m = [x y z θ] T , where x, y, and z are the three-dimensional coordinates and θ is the heading offset. The state and measurement model, f and h respectively, are similar to our previous publication [48]. State model is…”

Section: Infrastructure-free Cooperative Positioningmentioning

confidence: 99%

Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning

Mäkelä,

Olkkonen,

Kirkko-Jaakkola

et al. 2024

J. Ind. Sea. Pos. Nav.

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Ultra Wideband (UWB) signals are a promising choice for indoor positioning applications, since they are able to penetrate walls to a certain extent. Nevertheless, signal reflections and Non-Line-of-Sight (NLOS) propagation cause bias in the measured range. This ranging error can be corrected with Machine Learning (ML) methods such as Convolutional Neural Networks (CNNs). However, these ML models often generalize poorly between different environments. In this work we present an instance-based Transfer Learning (TL) approach, that enables generalizing a CNN-based ranging error mitigation approach to a new situation with only a few unlabeled training samples. The performance of the UWB error correction approach is demonstrated in a real-life infrastructure-free cooperative positioning setting.

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Section: Infrastructure-free Cooperative Positioningmentioning

confidence: 99%