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Hauptverfasser: Zhu, Hong, Venus, Alexander, Leitinger, Erik, Tertinek, Stefan, Witrisal, Klaus
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2403.16150
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author Zhu, Hong
Venus, Alexander
Leitinger, Erik
Tertinek, Stefan
Witrisal, Klaus
author_facet Zhu, Hong
Venus, Alexander
Leitinger, Erik
Tertinek, Stefan
Witrisal, Klaus
contents This paper addresses the challenge of achieving reliable and robust positioning of a mobile agent, such as a radio device carried by a person, in scenarios where direct line-of-sight (LOS) links are obstructed or unavailable. The human body is considered as an extended object that scatters, attenuates and blocks the radio signals. We propose a novel particle-based sum-product algorithm (SPA) that fuses active measurements between the agent and anchors with passive measurements from pairs of anchors reflected off the body. We first formulate radio signal models for both active and passive measurements. Then, a joint tracking algorithm that utilizes both active and passive measurements is developed for the extended object. The algorithm exploits the probabilistic data association (PDA) for multiple object-related measurements. The results demonstrate superior accuracy during and after the obstructed line-of-sight (OLOS) situation, outperforming conventional methods that solely rely on active measurements. The proposed joint estimation approach significantly enhances the localization robustness via radio sensing.
format Preprint
id arxiv_https___arxiv_org_abs_2403_16150
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fusion of Active and Passive Measurements for Robust and Scalable Positioning
Zhu, Hong
Venus, Alexander
Leitinger, Erik
Tertinek, Stefan
Witrisal, Klaus
Signal Processing
This paper addresses the challenge of achieving reliable and robust positioning of a mobile agent, such as a radio device carried by a person, in scenarios where direct line-of-sight (LOS) links are obstructed or unavailable. The human body is considered as an extended object that scatters, attenuates and blocks the radio signals. We propose a novel particle-based sum-product algorithm (SPA) that fuses active measurements between the agent and anchors with passive measurements from pairs of anchors reflected off the body. We first formulate radio signal models for both active and passive measurements. Then, a joint tracking algorithm that utilizes both active and passive measurements is developed for the extended object. The algorithm exploits the probabilistic data association (PDA) for multiple object-related measurements. The results demonstrate superior accuracy during and after the obstructed line-of-sight (OLOS) situation, outperforming conventional methods that solely rely on active measurements. The proposed joint estimation approach significantly enhances the localization robustness via radio sensing.
title Fusion of Active and Passive Measurements for Robust and Scalable Positioning
topic Signal Processing
url https://arxiv.org/abs/2403.16150