The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
새로운 기술인 무선 센서 네트워크를 사용하여 전자 장치를 가지고 있지 않은 대상을 감지하는 DFL(Device-Free Localization)은 보안 보호 장치, 스마트 홈 또는 병원과 같은 광범위한 응용 프로그램을 탄생시켰습니다. 이전 연구에서는 DFL을 분류 문제로 공식화했지만 정확성과 견고성 측면에서 여전히 몇 가지 과제가 있습니다. 본 논문에서는 매개변수를 사용하여 일반화된 임계값 알고리즘을 활용합니다. p DFL의 희소성 제약 조건이 있는 역 문제를 해결하기 위한 페널티 함수로 사용됩니다. 이 함수는 큰 계수에 더 적은 편향을 적용하고 값을 줄여 작은 계수에 페널티를 적용합니다. p. 차별화된 역량을 활용하여 p 희소성을 측정하기 위한 임계값 함수를 통해 제안된 접근 방식은 까다로운 환경에서 정확하고 강력한 위치 파악 성능을 달성할 수 있습니다. 광범위한 실험을 통해 이 알고리즘이 현재 대안보다 성능이 뛰어난 것으로 나타났습니다.
Qin CHENG
Nanjing University of Posts and Telecommunications,Jiangsu University of Technology
Linghua ZHANG
Nanjing University of Posts and Telecommunications
Bo XUE
Jiangsu University of Technology,Ministry of Education
Feng SHU
Nanjing University of Posts and Telecommunications
Yang YU
Jiangsu University of Technology
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부
Qin CHENG, Linghua ZHANG, Bo XUE, Feng SHU, Yang YU, "Device-Free Localization via Sparse Coding with a Generalized Thresholding Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 1, pp. 58-66, January 2022, doi: 10.1587/transcom.2021EBP3048.
Abstract: As an emerging technology, device-free localization (DFL) using wireless sensor networks to detect targets not carrying any electronic devices, has spawned extensive applications, such as security safeguards and smart homes or hospitals. Previous studies formulate DFL as a classification problem, but there are still some challenges in terms of accuracy and robustness. In this paper, we exploit a generalized thresholding algorithm with parameter p as a penalty function to solve inverse problems with sparsity constraints for DFL. The function applies less bias to the large coefficients and penalizes small coefficients by reducing the value of p. By taking the distinctive capability of the p thresholding function to measure sparsity, the proposed approach can achieve accurate and robust localization performance in challenging environments. Extensive experiments show that the algorithm outperforms current alternatives.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3048/_p
부
@ARTICLE{e105-b_1_58,
author={Qin CHENG, Linghua ZHANG, Bo XUE, Feng SHU, Yang YU, },
journal={IEICE TRANSACTIONS on Communications},
title={Device-Free Localization via Sparse Coding with a Generalized Thresholding Algorithm},
year={2022},
volume={E105-B},
number={1},
pages={58-66},
abstract={As an emerging technology, device-free localization (DFL) using wireless sensor networks to detect targets not carrying any electronic devices, has spawned extensive applications, such as security safeguards and smart homes or hospitals. Previous studies formulate DFL as a classification problem, but there are still some challenges in terms of accuracy and robustness. In this paper, we exploit a generalized thresholding algorithm with parameter p as a penalty function to solve inverse problems with sparsity constraints for DFL. The function applies less bias to the large coefficients and penalizes small coefficients by reducing the value of p. By taking the distinctive capability of the p thresholding function to measure sparsity, the proposed approach can achieve accurate and robust localization performance in challenging environments. Extensive experiments show that the algorithm outperforms current alternatives.},
keywords={},
doi={10.1587/transcom.2021EBP3048},
ISSN={1745-1345},
month={January},}
부
TY - JOUR
TI - Device-Free Localization via Sparse Coding with a Generalized Thresholding Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 58
EP - 66
AU - Qin CHENG
AU - Linghua ZHANG
AU - Bo XUE
AU - Feng SHU
AU - Yang YU
PY - 2022
DO - 10.1587/transcom.2021EBP3048
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2022
AB - As an emerging technology, device-free localization (DFL) using wireless sensor networks to detect targets not carrying any electronic devices, has spawned extensive applications, such as security safeguards and smart homes or hospitals. Previous studies formulate DFL as a classification problem, but there are still some challenges in terms of accuracy and robustness. In this paper, we exploit a generalized thresholding algorithm with parameter p as a penalty function to solve inverse problems with sparsity constraints for DFL. The function applies less bias to the large coefficients and penalizes small coefficients by reducing the value of p. By taking the distinctive capability of the p thresholding function to measure sparsity, the proposed approach can achieve accurate and robust localization performance in challenging environments. Extensive experiments show that the algorithm outperforms current alternatives.
ER -