Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm

Yu, Dongping; Guo, Yan; Li, Ning; Fang, Da‐Gang

doi:10.1109/access.2017.2649540

Cited by 18 publications

(13 citation statements)

References 34 publications

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“…However, the affected area of the wireless link is always limited and the target always independent with each other, making the probability that different targets being located at the detecting area of the same wireless link low. Following the most popular researches [13], [14], [28], we operate the target as independent options and assume that they do not affect the same wireless links.…”

Section: B the Cs-based Dfl Schemementioning

confidence: 99%

“…Then many types of researches are involved in the CS-based DFL scheme. The work in paper [14] exploits the dictionary refinement issue, which can well solve the multiple target localization issue. Paper [15] proposes a dictionary building approach that can build the dictionary in the new field using only a few samplings, which saves much human effort involved in building the dictionary.…”

Section: Introductionmentioning

confidence: 99%

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Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Guo

Yang

et al. 2020

IEEE Access

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In Device-Free Localization (DFL) schemes, most researches assume that the targets are stationary with fixed gestures and motions. However, objects always change their gestures in practice, undoubtedly influencing the localization accuracy. To solve the issue, we propose a new algorithm named Ges-DFL under the Compressive Sensing (CS) framework, which considers the time-varying target gestures in the DFL scheme. Firstly, leaning that the matrixes referring to different target gestures cannot be achieved, we transfer them to a fixed sensing matrix by the transferring method. Secondly, we build the localization scheme as an MMV recovery issue and exploit the block sparsity property of transferred location vectors to improve the localization accuracy. Thirdly, the new algorithm Ges-DFL scheme is designed under the framework of the variational Bayesian inference to reconstruct transferred location vectors. Simulations show that the proposed algorithm performs well both in localization accuracy and robustness.

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Section: B the Cs-based Dfl Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Guo

Yang

et al. 2020

IEEE Access

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“…In addition, the greatest advantage of RSS measurements is that they can explore and utilize the existing technologies and hardware. Although there have been many works exploring the multiplexing sensing mode of RF links for device-free localization [ 23 , 24 ] and activity recognition [ 25 , 27 , 28 , 29 ], the systematic design for acquiring biometrics generated by walking has not yet been formed.…”

Section: Related Studymentioning

confidence: 99%

“…In particular, compared with the traditional homogeneous optical imaging and infrared sensing, the RF signal-based sampling and measurement not only have the advantages of wide coverage, feasibility of penetrating obstacles, low cost, low power consumption, and flexible configuration, but also the superiorities of motion-specific sensing and privacy invasiveness. As a result, device-free radio sensing has recently become one of the most promising tomography imaging methods and has formed many practical applications, ranging from target localization and tracking in indoor and outdoor environment [ 23 , 24 ], through to respiratory monitoring [ 25 ], motion recognition [ 26 , 27 ], and elderly fall detection [ 28 , 29 ]. However, to the best of our knowledge, little literature has been focused on the biometric sensing of walking modality by using RF links.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links

Liu

Liang

2017

Sensors

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This article proposes a lightweight biometric sensing system using ubiquitous narrowband radio frequency (RF) links for path-dependent walker classification. The fluctuated received signal strength (RSS) sequence generated by human motion is used for feature representation. To capture the most discriminative characteristics of individuals, a three-layer RF sensing network is organized for building multiple sampling links at the most common heights of upper limbs, thighs, and lower legs. The optimal parameters of sensing configuration, such as the height of link location and number of fused links, are investigated to improve sensory data distinctions among subjects, and the experimental results suggest that the synergistic sensing by using multiple links can contribute a better performance. This is the new consideration of using RF links in building a biometric sensing system. In addition, two types of classification methods involving vector quantization (VQ) and hidden Markov models (HMMs) are developed and compared for closed-set walker recognition and verification. Experimental studies in indoor line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios are conducted to validate the proposed method.

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“…It can realize multi-target DFL without knowing the number of targets. Moreover, to mitigate the influence of the environmental dynamics in changing environments, DR-DFL [ 21 ] conducts joint location estimation and dictionary refinement based on the variational expectation-maximization (VEM) [ 22 , 23 ] algorithm.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

Guo

2018

Sensors

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Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

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Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm

Cited by 18 publications

References 34 publications

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive Sensing

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links

Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

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