Enhancing the Performance of Indoor Device-Free Passive Localization

Yang, Wu; Gong, Liangyi; Man, Dapeng; Lv, Jiguang; Cai, Haibin; Zhou, Xiancun; Yang, Zheng

doi:10.1155/2015/256162

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…To study the various possibilities of Wi-Fi signals, this paper collects CSI signals to construct a fingerprint dataset. Compared with the RSSI, CSI can provide higher-dimensional and more fine-grained information [ 19 , 20 ]. In addition, compared with other papers using multiple APs (access points) to construct an RSSI reading matrix, this paper collects only CSI signals between a single AP and mobile devices to build the fingerprint dataset.…”

Section: Introductionmentioning

confidence: 99%

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Yin

Yang

et al. 2021

Sensors

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Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

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Section: Introductionmentioning

confidence: 99%

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Yin

Yang

et al. 2021

Sensors

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“…However, since there are usually a small number of devices, typically one AP, installed in a large class of scenarios such as homes, an alternative approach might be to study the sensitivity of the receiver, as explored in Refs. [29,30], and to improve system performance by choosing the best receiver location and multipath adaptation.…”

Section: Discussionmentioning

confidence: 99%

Robust and passive motion detection with COTS WiFi devices

Zhu

Xiao

Sun

et al. 2017

Tinshhua Sci. Technol.

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Device-free Passive (DfP) detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength (RSS), most recent studies rely on finer-grained Channel State Information (CSI). However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight (LOS) or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection (R-PMD). Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis (PCA)-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

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“…Due to the excessive offset error [24,25] included in CSI phase, phase sanitization is required to effectively utilize CSI phase [9]. The measured phase P ci can be linearly transformed to obtain raw phase P zi : Compared with P ci , P zi eliminates the random phase offset and has effective features necessary for intrusion detection in true phase simultaneously.…”

Section: Data Preprocessing (1) Phase Sanitizationmentioning

confidence: 99%

IDSDL: a sensitive intrusion detection system based on deep learning

Bai

Yang

et al. 2021

J Wireless Com Network

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Device-free passive (DfP) intrusion detection system is a system that can detect moving entities without attaching any device to the entities. To achieve good performance, the existing algorithms require proper access point (AP) deployment. It limits the applying scenario of those algorithms. We propose an intrusion detection system based on deep learning (IDSDL) with finer-grained channel state information (CSI) to free the AP position. A CSI phase propagation components decomposition algorithm is applied to obtain blurred components of CSI phase on several paths as a more sensitive detection signal. Convolutional neuron network (CNN) of deep learning is used to enable the computer to learn and detect intrusion without extracting numerical features. We prototype IDSDL to verify its performance and the experimental results indicate that IDSDL is effective and reliable.

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Enhancing the Performance of Indoor Device-Free Passive Localization

Cited by 18 publications

References 28 publications

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Robust and passive motion detection with COTS WiFi devices

IDSDL: a sensitive intrusion detection system based on deep learning

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