Adaptive Device-Free Localization in Dynamic Environments Through Adaptive Neural Networks

Zhou, Rui; Hou, Huanhuan; Gong, Ziyuan; Chen, Zuona; Tang, Kai; Zhang, Bao

doi:10.1109/jsen.2020.3014641

Cited by 28 publications

(9 citation statements)

References 37 publications

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“…Li et al [25] developed a centralized indoor localization method using pseudo-label along with federated learning for the improved indoor localization. AdapLoc [26] utilized the CNN and domain adaptation for the device-free WiFi localization in dynamic environments. In contrast, our work proposes to apply deep learning to LiDAR sensors for global localization.…”

Section: B Learning-based Localization Systemsmentioning

confidence: 99%

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

Wang

Zhao

et al. 2022

IEEE Sensors J.

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In this paper, we present a novel end-to-end learning-based LiDAR sensor relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input. Compared to visual sensorbased relocalization, LiDAR sensors can provide rich and robust geometric information about a scene. However, point clouds of LiDAR sensors are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360 • LiDAR sensor frames. Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposed method can achieve accurate relocalization performance.

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Section: B Learning-based Localization Systemsmentioning

confidence: 99%

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

Wang

Zhao

et al. 2022

IEEE Sensors J.

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“…The approach combined the proposed transfer learning model with the ensemble approach to avoid overfitting and unfitting problems. In reference [23], the DA in localization based one-dimension Convolutional Neural Network (1D-CNN) is realized with Semantic Alignment (SA), moreover, a domain selection model is also trained to change pattern of system, and thus the localization results are more reliable. In reference [41], features squeezing and Class Alignment (CA) Loss are utilized to maximize the distance between different classes in positioning system.…”

Section: B Domain Adaptation In Fingerprint Localizationmentioning

confidence: 99%

“…After finishing training, the learned features should be domain-invariant and prediction-discriminative. In addition, due to 1D feature of fingerprint, the experiments in reference [23] show that 1D-CNN outperforms 2D-CNN and DNN in Wi-Fi fingerprint signal processing. Therefore, in order to integrate merits of AE and 1D-CNN, the 1D-Covolutional Autoencoder (1D-CAE) shown in Fig.…”

Section: Adaptive Wi-fi Based Localization a 1d-cae Feature Extractormentioning

confidence: 99%

“…In order to eliminate this situation, various techniques are proposed to improve stabilization of fingerprint positioning system. Domain Adaptation (DA) in Transfer Learning (TL) is popular in solving fingerprint adaptation in dynamic environment [23]. The approach transfers the features of source domain (the initial environment) and target domain (the changed environment) into the same feature space using supervised or semi-supervised learning.…”

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confidence: 99%

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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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“…Chen et al [ 26 ] used two CNN to realize indoor localization, called a two-stage CNN deep learning approach; one was used to identify the inherent features of an environment, based on first CNN recognition results (choosing an appropriate positioning model), the other one was applied to realize localization. Zhou et al [ 27 ] proposed a method named AdapLoc which was based on one-dimensional Convolutional Neural Network (1D-CNN) to dynamically adapt to environmental change, and the evaluation experiment verified the effectiveness of AdapLoc. Zhao et al [ 28 ] designed a hybrid convolutional autoencoder neural network to extract the features of location-related signals, and the experiments showed that the convolutional autoencoder neural network not only worked well in a real world dataset but also had anti-noise ability and low latency (average 4 ms).…”

Section: Related Workmentioning

confidence: 99%

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

Long

Kong

Zhang

et al. 2021

Sensors

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A fingerprint-based localization system is an economic way to solve an indoor positioning problem. However, the traditional off-line fingerprint collection stage is a time-consuming and laborious process which limits the use of fingerprint-based localization systems. In this paper, based on ubiquitous Wireless Fidelity (Wi-Fi) equipment and a low-cost Ultra-Wideband (UWB) ranging system (with only one UWB anchor), a ready-to-use indoor localization system is proposed to realize long-term and high-accuracy indoor positioning. More specifically, in this system, it is divided into two stages: (1) an initial stage, and (2) a positioning stage. In the initial stage, an Inertial Measure Unit (IMU) is used to calculate the position using Pedestrian Dead Reckon (PDR) algorithm within a preset number of steps, and the location-related fingerprints are collected to train a Convolutional Neural Network (CNN) regression model; simultaneously, in order to make the UWB ranging system adapt to the Non-Line-of-Sight (NLoS) environment, the increments of acceleration and angular velocity in IMU and the increments of single UWB ranging measures are correlated to pre-train a Supported Vector Regression (SVR). After reaching the threshold of time or step number, the system is changed into a positioning stage, and the CNN predicts the position calibrated by corrected UWB ranging. At last, a series of practical experiments are conducted in the real environment; the experiment results show that, due to the corrected UWB ranging measures calibrating the CNN parameters in every positioning period, this system has stable localization results in a comparative long-term range. Additionally, it has the advantages of stability, low cost, anti-noise, etc.

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Adaptive Device-Free Localization in Dynamic Environments Through Adaptive Neural Networks

Cited by 28 publications

References 37 publications

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

The Adaptive Fingerprint Localization in Dynamic Environment

A CSI-Based Indoor Positioning System Using Single UWB Ranging Correction

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