Device-Free Localization Using Enhanced Channel Selection and a Distance-Based Elliptical Model

Li, Guoping; Lei, Qian

doi:10.1109/access.2022.3228837

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…To cover the target area with a sufficient amount of MPCs, we placed four sensor nodes that were equipped with a UWB radio in the target area. With four sensors, we could localize positions that were in the vicinity of several MPCs with an accuracy of less than 1 m. In addition, with a more realistic weight function for MA-RTI (such as proposed in [ 18 ]), the localization accuracy would be increased in the future.…”

Section: Discussionmentioning

confidence: 99%

“…As stated before, DFL systems must cope with multipath propagation, otherwise the multipath propagation decreases the localization accuracy. Some authors avoid multipath by measuring the channel measurement over various channels in order to combine the information, or pick the channel that is least affected by multipath [ 4 , 5 , 18 ].…”

Section: Principles Of Device-free Localization and Related Workmentioning

confidence: 99%

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MA-RTI: Design and Evaluation of a Real-World Multipath-Assisted Device-Free Localization System

Cimdins

Schmidt

John

et al. 2023

Sensors

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Device-free localization (DFL) systems exploit changes in the radio frequency channel by measuring, for example, the channel impulse response (CIR), to detect and localize obstacles within a target area. However, due to a lack of well-defined interfaces, missing modularization, as well as complex system configuration, it is difficult to deploy DFL systems outside of laboratory setups. This paper focused on the system view and the challenges that come with setting up a DFL system in an indoor environment. We propose MA-RTI, a modular DFL system that is easy to set up, and which utilizes a multipath-assisted (MA) radio-tomographic imaging (RTI) algorithm. To achieve a modular DFL system, we proposed and implemented an architectural model for DFL systems. For minimizing the configuration overhead, we applied a 3D spatial model, that helps in placing the sensors and calculating the required calibration parameters. Therefore, we configured the system solely with idle measurements and a 3D spatial model. We deployed such a DFL system and evaluated it in a real-world office environment with four sensor nodes. The radio technology was ultra-wideband (UWB) and the corresponding signal measurements were CIRs. The DFL system operated with CIRs that provided a sub-nanosecond time-domain resolution. After pre-processing, the update rate was approximately 46 Hz and it provided a localization accuracy of 1.0 m in 50 % of all cases and 1.8 m in 80 % of all cases. MA fingerprinting approaches lead to higher localization accuracy, but require a labor-intensive training phase.

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Section: Discussionmentioning

confidence: 99%