A Training-Free Multipath Enhancement (TFME-RTI) Method for Device-Free Multi-Target Localization

Zhang, Xiaoman; Ma, Yongtao; Gong, Xiaolin; Fu, Haoyu; Wang, Bobo; Ning, Wanru; Liang, Xiuyan

doi:10.1109/jsen.2022.3154591

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…8, the voxel values closer to the actual location of the target are emphasized much more than those at other locations, providing visual confirmation of the target's presence, and at…”

mentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

“…However, only a few studies have been conducted on multipath-propagation-based RTI [8]- [11]. In [8], the feasibility study of applying static reflection multipath to devicefree localization is presented. In [9], low-rank and sparse decomposition were used to remove image artifacts caused by multipath and improved the accuracy of RTI images.…”

Section: Introductionmentioning

confidence: 99%

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Multipath-RTI: Millimeter-Wave Radio Based Device-Free Localization

Ikegami,

Kim,

Miyake

et al. 2024

Preprint

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This paper presents a novel localization technique—Multipath-radio tomographic imaging (RTI)—based on millimeterwave (mm-wave) radio systems, such as 5G mobile and WiGig LAN systems. Because of their high delay and angle resolutions, mm-wave radio systems can realize highly accurate device-free localization (DFL) for smart home/smart building applications. They can thus easily separate the multipath components. The Multipath-RTI can drastically reduce the number of anchor nodes using virtual nodes created by multipaths. This paper established the signal processing procedure for RTI image generation and position estimation using Multipath-RTI. Then, a practical signal processing scheme, ray-tracing-assisted Multipath-RTI, is presented, and the DFL results obtained by multilink double-directional channel sounding measurements are shown. Here, the position estimation errors in the measurements were less than 0:5 m for all target positions. Furthermore, the empirical position estimation errors for a single target under various conditions obtained by simulations were less than 0:5 m in 96.2 % and 88.0 % of the areas in an L-shaped room and a room with an obstacle in the center, respectively. The position estimation accuracy for multiple targets was also evaluated, where, for example, the errors for three targets were less than 0.1 m at 50 % probability with failure rates of less than 20 %.

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“…8, the voxel values closer to the actual location of the target are emphasized much more than those at other locations, providing visual confirmation of the target's presence, and at…”

mentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multipath-RTI: Millimeter-Wave Radio Based Device-Free Localization

Ikegami,

Kim,

Miyake

et al. 2024

Preprint

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“…There are five Classical algorithms extensively used in DFL systems, e.g., the fingerprint (FP) [17]- [20], radio tomographic imaging (RTI) [1], [15], [21]- [24], the support vector machine (SVM) [25]- [27], the Bayesian system [16], [28], [29], and compressed sensing (CS) [30]- [32]. Algorithms studied in the literature are presented, as summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…The monitoring area was divided into voxels. The Narrowband Relative Ultra-Wideband [10], [11], [12] UWB Relative Localization from RSS [4], [5], [6], [7], [13], [14], [15], [16] RSS Absolute Fingerprint-Matching [3], [17], [18], [19], [20] RSS Absolute Radio Tomographic Imaging [1], [2], [15], [21], [22], [23], [24], [33] RSS Absolute Support Vector Machine [25], [26], [27] RSS Absolute Bayesian system [16], [28], [29] RSS Relative Compressed Sensing [30], [31], [32] RSS Relative weightings of voxels inside one elliptical weight mode were the same, which was not consistent with the actual situation. Consequently, several researchers studied the elliptical weight model for improvement in localization estimation accuracy in RTI.…”

Section: Introductionmentioning

confidence: 99%

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

Lei

2022

IEEE Access

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Device-free localization (DFL) based on wireless sensor networks (WSNs) is a technology that can detect and locate a person by measuring the changes in received signals without the need for any wireless devices. As an emerging important technology in WSNs, radio tomographic imaging (RTI) has received increasing attention. However, there is much room to improve localization accuracy in RTI. To address this issue, an enhanced channel-selection method and a new distance-based elliptical model are proposed to improve the localization accuracy. The enhanced frequency channel-selection method selects two channels with the lowest received signal strength (RSS) variances to collect data. This approach is more robust to environmental change. The new distance-based elliptical model is based on the distance between the voxels and sensors. Meanwhile, the communication links are divided into line-of-sight (LOS) paths and nonline-of-sight (NLOS) paths. Experimental results demonstrate that the proposed algorithm improves the accuracy of positioning by up to 44.8% over some state-of-the-art RTI methods with low cost.

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