Bayesian Approaches to Multipath-Enhanced Device-Free Localization

Schmidhammer, Martin; Siebler, Benjamin; Gentner, Christian; Sand, Stephan; Fiebig, U.-C.

doi:10.23919/eucap51087.2021.9411290

Cited by 5 publications

(14 citation statements)

References 10 publications

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“…In Ref. [11], we have presented corresponding non‐linear Bayesian filter solutions required to realise MDFL. These filter solutions have been evaluated by simulations.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

Schmidhammer

Siebler

Gentner

et al. 2022

IET Microwaves Antenna & Prop

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Device-free localisation (DFL) systems infer presence and location of moving users by measuring to which extent they change the received signal power in wireless links. Consequently, users not only induce perturbations to the power of the line of sight but also to the power of reflected and scattered signals which are observed in the received signal as multipath components (MPCs). Since the propagation paths of MPCs differ inherently from the line-of-sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath-enhanced device-free localisation (MDFL) system. Based on empirical models that relate perturbations in the received power of MPCs to the user location, the localisation problem can be solved by non-linear Bayesian filtering. In this work, we investigate the point mass filter and the particle filter as possible solutions. We demonstrate the applicability of these solutions using ultra-wideband measurements and develop and verify a numerical simulation framework that flexibly enables a sound evaluation of MDFL. Based on both measurements and simulations, we show a significant improvement of the localisation performance of MDFL compared to DFL. The overall localisation performance is thereby comparable for both filters. Eventually, we show that complexity and divergence probability, rather than localisation performance, are the decisive factors for the choice of the filter solution.

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“…In Ref. [11], we have presented corresponding non‐linear Bayesian filter solutions required to realise MDFL. These filter solutions have been evaluated by simulations.…”

Section: Introductionmentioning

confidence: 99%

“…In order to verify the numerical simulation framework used in Ref. [11], we have conducted an indoor measurement campaign as part of this work. In particular, we measured the radio channel with commercial off‐the‐shelf (COTS) UWB devices, which enable us to derive and verify this numerical simulation framework based on measurements.…”

Section: Introductionmentioning

confidence: 99%

Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

Schmidhammer

Siebler

Gentner

et al. 2022

IET Microwaves Antenna & Prop

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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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“…In [16], we have adapted the diffraction based model of [18] for multipath propagation. Requiring a computational more efficient model, in [17] and [21], we have approximated the fading of MPCs by an exponential model.…”

Section: Introductionmentioning

confidence: 99%

Empirical Fading Model and Bayesian Calibration for Multipath-Enhanced Device-Free Localization

Schmidhammer¹,

Gentner²,

Walter³

et al. 2022

Preprint

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Bayesian Approaches to Multipath-Enhanced Device-Free Localization

Cited by 5 publications

References 10 publications

Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

Bayesian multipath‐enhanced device‐free localisation: Simulation‐ and measurement‐based evaluation

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

Empirical Fading Model and Bayesian Calibration for Multipath-Enhanced Device-Free Localization

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