A fine-grained indoor localization using multidimensional Wi-Fi fingerprinting

Deng, Chao; Du, Li; Jiang, Zhiping; Xi, Wei; Han, Jinsong; Zhao, Kun; Zhao, Jizhong; Wang, Zhi; Li, Rui

doi:10.1109/padsw.2014.7097846

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…First, integrating Eqs. ( 7)- (11), and initializing a(h i,j , h k,j ) as 0. Second, updating r and a iteratively, until the number of iterations exceeds the maximum or the iteration results are stable in the last few iterations.…”

Section: ) Calculate Effective Featuresmentioning

confidence: 99%

“…In 2012, Jiang Xiao et al proposed FIFS [10] to perform weighted averaging on CSI amplitude data from three antennas, making full use of the diversity of antennas and sub-carriers for positioning. In 2014, Chen et al [11] proposed to construct an integrated fingerprint include CSI, RSS and transmitted power to enrich features. Chapre et al [12] aggregated CSI over multiple antennas, treated the deviation between subsequent sub-carriers as new features.…”

Section: Introductionmentioning

confidence: 99%

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C-Map: Hyper-Resolution Adaptive Preprocessing System for CSI Amplitude-Based Fingerprint Localization

et al. 2019

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With the increasing popularity of location-based services, channel state information (CSI) has received widespread attention in positioning due to the fine-grained information it provides. However, the raw amplitudes of CSI are especially sensitive to noise and easily effected by the frequency-selective fading. As the expanding of test area, positioning is disturbed by random noise and insufficient resolution of features severely. In this paper, we propose Cluster-Mapping (C-Map), an adaptive pre-processing system for CSI amplitude-based fingerprint localization. This system models positioning as a classification problem, an adaptive processing mechanism is constructed according to the characteristics of CSI amplitude. C-Map mainly contains two parts: dynamic denoising and feature enhancement. In the dynamic denoising, effective features are screened out by clustering iteratively, without specifying parameters according to the experimental environment. In the feature enhancement, polynomial fitting with regularization is used to reduce jagged fluctuations, then the trend of variation over 30 sub-carriers are described by combining derivation with nonlinear kernel function. Extensive experiments are conducted in typical environments to verify the superior performance of C-Map for the preprocessing of CSI amplitude. Compared with the combination of mean and multidimensional scaling (MDS), the average positioning error of C-Map is reduced 28.1% in comprehensive indoor environment, and 19% in garage. Furthermore, compared with the combination of DBSCAN and principal component analysis (PCA), the average positioning error of C-Map is reduced 33.1% in comprehensive indoor environment, and 28% in garage.INDEX TERMS Channel state information (CSI), feature enhancement, data pre-processing, wireless communication, indoor localization.

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Section: ) Calculate Effective Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

C-Map: Hyper-Resolution Adaptive Preprocessing System for CSI Amplitude-Based Fingerprint Localization

et al. 2019

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“…FIFS 22 combines CSI with the spatial diversity of multi-antennas as indoor positioning fingerprints and proposes a coherence-bandwidth-enhanced probability algorithm with a correlation filter to map objects into the fingerprint database. RSS, the transmitted power, and channel information are employed to construct an integrated fingerprint, and a cosine-similarity-based matching algorithm and an enhanced particle filter mechanism are designed to obtain accurate positioning and tracking; 23 CSI-MIMO 24 jointly integrates CSI with Multiple-Input Multiple-Output (MIMO) information as fingerprints to localize precisely. Indoor localization methods mentioned above all assume a clear pre-knowledge on PA, especially PA is unchanged during the whole localization.…”

Section: Related Workmentioning

confidence: 99%

Fine-grained wireless propagation ambience sensing

Jing

Sun

Wang

et al. 2018

International Journal of Distributed Sensor Networks

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The ubiquitous wireless network infrastructure and the need of people's indoor sensing inspire the work leveraging wireless signal into broad spectrum for indoor applications, including indoor localization, human-computer interaction, and activity recognition. To provide an accurate model selection or feature template, these applications take the system reliability of the signal in line-of-sight and non-line-of-sight propagation into account. Unfortunately, these two types of signal propagation are analyzed in static or mobile scenario separately. Our question is how to use the wireless signal to estimate the signal propagation ambience to facilitate the adaptive complex environment? In this paper, we exploit the Fresnel zone theory and channel state information (CSI) to model the static and mobile ambience detectors. Considering the spatiotemporal correlation of indoor activities, the propagation ambience can be divided into three categories: line-of-sight (LOS), non-line-of-sight (NLOS), and semi-line-of-sight (SLOS), which is used to represent the intermediate state between the LOS and NLOS propagation ambience during user movement. Leveraging the hidden Markov model to estimate the dynamic propagation ambience in the mobile environment, a novel propagation ambience identification method, named Ambience Sensor (Asor), is proposed to improve the real-time performance for the upper applications. Furthermore, Asor is integrated into a localization algorithm, Asor-based localization system (Aloc), to confirm the effectiveness. We prototype Asor and Aloc based on commodity WiFi infrastructure without any hardware modification. In addition, the real-time performance of Asor is evaluated by conducting tracking experiments. The experimental results show that the median detection rate of propagation ambience is superior to the existing methods in absence of any a priori hypothesis of static or mobile scenarios.

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