Accuracy-aware wireless indoor localization: Feasibility and applications

Luo, Chengwen; Hong, Hande; Cheng, Long; Chan, Mun Choon; Li, Jianqiang; Ming, Zhong

doi:10.1016/j.jnca.2015.12.008

Cited by 23 publications

(7 citation statements)

References 20 publications

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“…19 It depends on a set of the strongest APs that have a high probability to cover the entire area. Luo et al 20 applied the Gaussian process–based approach to model the signal distribution of APs and characterize radio map fingerprints in the entire indoor environment. The estimated localization error depended on the fingerprint sampling and error estimation algorithm.…”

Section: Related Workmentioning

confidence: 99%

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

Alhammadi¹,

Hashim²,

Rasid³

et al. 2020

International Journal of Distributed Sensor Networks

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Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

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Section: Related Workmentioning

confidence: 99%

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

Alhammadi¹,

Hashim²,

Rasid³

et al. 2020

International Journal of Distributed Sensor Networks

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“…工业云是通过信息资源整合为工业提供服务支持的一种智能服务, 通过云计算、物联网和工业软件等技术手段, 将人、机、物、知识等有机结合, 向用户提供资源和能力共享服务等 [2] . 工业大数据 14) 处理云平台通过将工业大数据与工业云结合, 以实现物理设备与虚拟网络融合的数据采集、传输、协同处理和应用集成为目标, 需通过云端融合关键技术解决工业大数据处理实时性、精准性等重要问题, 达成工业大数据的处理分析决策与反馈控制的智能化和柔性化 [66,67] .…”

Section: 工业云计算和大数据unclassified

The architecture and key technologies for an industrial Internet with synergy between the cloud and clients

Luo¹,

He²,

Zhang³

et al. 2020

Sci. Sin.-Inf.

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“…RFID Antenna Figure 1: The general idea of the proposed DfP localization and tracking system considered to be privacy invasive [1]. Another DfP localization technique is to intensively exploit the radio-frequency signal, e.g., localizing the target by analyzing the Received Signal Strength (RSS) variations [17,18,2] or Channel State Information (CSI) [19,20] in WIFI, or tracking the user through a wall by decoding the radiowaves reflected of human movement [21]. Though promising, these systems often require specialized RF signals such as Frequency-Modulated Continuous Wave (FMCW) or build upon costly special-purpose devices such as USRP (universal software radio peripheral), or need to modify the low-level firmware such as abstracting CSI signals.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…With the explosively increasing aging population, intelligent space that can better support the independent living of the elderly has been attracting the increasing attention both from industry and academia. One of the key preconditions for such a smart environment lies on an accurate and timely detection of users' locations and daily routines [1,2], especially for an indoor environment that GPS (Global Position System) cannot handle [3]. To tackle this challenge, a wide range of indoor localization and tracking systems have been proposed for the last two decades, including but not limited to LANDMARC [4], WILL [5], Tagoram [6] and BackPos [7].…”

Section: Introductionmentioning

confidence: 99%

Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach

Ruan

Sheng

Yao

et al. 2018

Journal of Network and Computer Applications

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Localizing and tracking human movement in a device-free and passive manner is promising in two aspects: i) it neither requires users to wear any sensors or devices, ii) nor it needs them to consciously cooperate during the localization. Such indoor localization technique underpins many real-world applications such as shopping navigation, intruder detection, surveillance care of seniors etc. However, current passive localization techniques either need expensive/sophisticated hardware such as ultra-wideband radar or infrared sensors, or have an issue of invasion of privacy such as camera-based techniques, or need regular maintenance such as the replacement of batteries. In this paper, we build a novel data-driven localization and tracking system upon a set of commercial ultrahigh frequency passive radio-frequency identification tags in an indoor environment. Specifically, we formulate human localization problem as finding a location with the maximum posterior probability given the observed received signal strength indicator from passive radio-frequency identification tags. In this regard, we design a series of localization schemes to capture the posterior probability by taking the advance of supervised-learning models including Gaussian Mixture Model, k Nearest Neighbor and Kernel-based Learning. For tracking a moving target, we mathematically model the task as searching a location sequence with the most likelihood, in which we first augment the probabilistic estimation learned in localization to construct the Emission Matrix and propose two human mobility models to approximate the Transmission Matrix in the Hidden Markov Model. The proposed tracking model is able to transfer the pattern learned in localization into tracking but also reduce the location-state candidates at each transmission iteration, which increases both the computation efficiency and tracking accuracy. The extensive experiments in two real-world scenarios reveal that our approach can achieve up to 94% localization accuracy and an average 0.64m tracking error, outperforming other state-of-the-art radio-frequency identification based indoor localization systems.

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Accuracy-aware wireless indoor localization: Feasibility and applications

Cited by 23 publications

References 20 publications

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

The architecture and key technologies for an industrial Internet with synergy between the cloud and clients

Device-free human localization and tracking with UHF passive RFID tags: A data-driven approach

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