Convolutional neural networks based indoor Wi-Fi localization with a novel kind of CSI images

Li, Haihan; Zeng, Xiangsheng; Li, Yunzhou; Zhou, Shidong; Wang, Jing

doi:10.23919/jcc.2019.09.019

Cited by 45 publications

(19 citation statements)

References 3 publications

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“…In reference [33], a CNN-LSTM hybrid model is proposed to provide stable localization results using both temporal information (sliding window processing) and spatial information (converting sequence to picture) of CSI signals, and the positioning accuracy is about 2.5 meters. Different from most works, the reference [34] concentrates on constructing robust positioning characteristics, where the phase differences and amplitude differences of CSI are used to construct three gray images, after that, the three grayscale images are fused into one RGB image for CNN identification and positioning. In reference [35], the depth-wise separable convolution is used to simplify CSI-based localization model, which can reduce latency and improve performance of the system.…”

Section: Related Work a Traditional Positioning Technologiesmentioning

confidence: 99%

The Adaptive Fingerprint Localization in Dynamic Environment

Long

Zheng²,

Zhang

et al. 2022

IEEE Sensors J.

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Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.

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Section: Related Work a Traditional Positioning Technologiesmentioning

confidence: 99%

The Adaptive Fingerprint Localization in Dynamic Environment

Long

Zheng²,

Zhang

et al. 2022

IEEE Sensors J.

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“…It creates two 3D images of the calibrated phase and amplitude of the CSI, where the height, the width, and the depth (number of channels) are, respectively, the amplitude/phase from different packets, different sub-carriers (30 available) and Tx-Rx antenna combinations. In a three-antenna system, [232] creates three-channel images that have the amplitudes of all sub-carrier × packet samples in one channel, and pair-wise phase differences between the antenna signals in the two other channels. They use ShuffleNet, a computationally efficient deep CNN architecture, to predict the most probable RPs.…”

Section: Supervisedmentioning

confidence: 99%

“…For instance, to collect the raw CSI signal, several papers rely on Intel WiFi link 5300 NIC chipset [359]. They use modified chipset firmware [231], [232], [300], [331], which provides access to the CSI at three antennas. To use the FTM protocol, [194] uses the Intel AC8260 WiFi chipset that supports FTM functionality [360].…”

Section: A Used Datasetsmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

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The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets.A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field.This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

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“…Hsieh et al used RSS and channel state information (CSI) as CNN data input [ 27 ]. Li et al used AOA, TOA, and amplitude of CSI data sources to build RGB images to obtain higher positioning accuracy [ 28 ]. Sinha and Li et al have achieved a good positioning effect by using the CNN method for indoor positioning.…”

Section: Introductionmentioning

confidence: 99%

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Cheng

Niu

Zhang

et al. 2022

Sensors

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With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.

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Convolutional neural networks based indoor Wi-Fi localization with a novel kind of CSI images

Cited by 45 publications

References 3 publications

The Adaptive Fingerprint Localization in Dynamic Environment

The Adaptive Fingerprint Localization in Dynamic Environment

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

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