scholarly journals Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Liye Zhang ◽  
Xiaoliang Meng ◽  
Chao Fang
Keyword(s):  
Linear Regression ◽  
Error Detection ◽  
Theoretical Study ◽  
Indoor Localization ◽  
Signal Strength ◽  
Probability Of Error ◽  
Localization Accuracy ◽  
Localization System ◽  
Radio Map ◽  
Localization Systems

Recent years have witnessed a growing interest in using WLAN fingerprint-based methods for the indoor localization system because of their cost-effectiveness and availability compared to other localization systems. In this system, the received signal strength (RSS) values are measured as the fingerprint from the access points (AP) at each reference point (RP) in the offline phase. However, signal strength variations across diverse devices become a major problem in this system, especially in the crowdsourcing-based localization system. In this paper, the device diversity problem and the adverse effects caused by this problem are analyzed firstly. Then, the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression (LR) algorithm. Based on the analysis, the LR algorithm is proposed to create a unique radio map in the offline phase and precisely estimate the user’s location in the online phase. After applying the LR algorithm in the crowdsourcing systems, the device diversity problem is solved effectively. Finally, we verify the LR algorithm using the theoretical study of the probability of error detection. Experimental results in a typical office building show that the proposed method results in a higher reliability and localization accuracy.

scholarly journals Noise reduction for radio map crowdsourcing building in WLAN indoor localization system

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Liye Zhang ◽  
Zhuang Wang ◽  
Xiaoliang Meng ◽  
Chao Fang ◽  
Cong Liu
Keyword(s):  
Indoor Localization ◽  
Sensor Data ◽  
Mode Switching ◽  
Map Building ◽  
Position Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Straight Line ◽  
Radio Map ◽  
Localization Systems

AbstractRecent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost-effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor localization system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduce the noise in the location space of the radio map, the short-term Fourier transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

scholarly journals DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chong Han ◽  
Wenjing Xun ◽  
Lijuan Sun ◽  
Zhaoxiao Lin ◽  
Jian Guo
Keyword(s):  
Indoor Localization ◽  
Wireless Sensing ◽  
Training Phase ◽  
Training Time ◽  
Localization Accuracy ◽  
Localization System ◽  
Complex Models ◽  
Indoor Scenarios ◽  
Trained Network ◽  
Localization Systems

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

scholarly journals Noise Reduction for Radio Map Crowdsourcing Building in WLAN Indoor Localization System

2021 ◽  
Author(s):  
liye zhang ◽  
Zhuang Wang ◽  
Xiaoliang Meng ◽  
Chao Fang ◽  
Cong Liu
Keyword(s):  
Indoor Localization ◽  
Sensor Data ◽  
Mode Switching ◽  
Map Building ◽  
Position Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Straight Line ◽  
Indoor Positioning System ◽  
Radio Map

Abstract Recent years have witnessed a growing interest in using WLAN fingerprint-based method for indoor localization system because of its cost effectiveness and availability compared to other localization systems. In order to rapidly deploy WLAN indoor positioning system, the crowdsourcing method is applied to alternate the traditional deployment method. In this paper, we proposed a fast radio map building method utilizing the sensors inside the mobile device and the Multidimensional Scaling (MDS) method. The crowdsourcing method collects RSS and sensor data while the user is walking along a straight line and computes the position information using the sensor data. In order to reduces the noise in the location space of the radio map, the Short Term Fourier Transform (STFT) method is used to detect the usage mode switching to improve the step determination accuracy. When building a radio map, much fewer RSS values are needed using the crowdsourcing method compared to conventional methods, which lends greater influence to noises and erroneous measurements in RSS values. Accordingly, an imprecise radio map is built based on these imprecise RSS values. In order to acquire a smoother radio map and improve the localization accuracy, the MDS method is used to infer an optimal RSS value at each location by exploiting the correlation of RSS values at nearby locations. Experimental results show that the expected goal is achieved by the proposed method.

scholarly journals Graph-based Semi-supervised Learning for Indoor Localization Using Crowdsourced Data

2017 ◽  
Author(s):  
Liye Zhang ◽  
Shahrokh Valaee ◽  
Yu Bin Xu ◽  
Lin Ma ◽  
Farhang Vedadi
Keyword(s):  
Supervised Learning ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Propagation Model ◽  
Localization Accuracy ◽  
Crowdsourced Data ◽  
Negative Effect ◽  
Rapid Deployment ◽  
Radio Map ◽  
Localization Systems

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

scholarly journals An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2857
Author(s):  
Simon Tomažič ◽  
Igor Škrjanc
Keyword(s):  
Data Acquisition ◽  
Indoor Localization ◽  
Low Cost ◽  
Path Loss ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization System ◽  
Good Trade ◽  
Strength Modeling

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

The WiMap

2013 ◽  
pp. 31-41
Author(s):  
Junjun Xu ◽  
Haiyong Luo ◽  
Fang Zhao ◽  
Rui Tao ◽  
Yiming Lin ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Signal Strength ◽  
Gaussian Mixture ◽  
Threshold Control ◽  
Localization Accuracy ◽  
Signal Distribution ◽  
Localization System ◽  
Design And Implementation ◽  
The Internet Of Things ◽  
Grid Based

As positioning technology is an important foundation of the Internet of Things, a dynamic indoor WLAN localization system is proposed in this paper. This paper mainly concentrates on the design and implementation of the WiMap-a dynamic indoor WLAN localization system, which employs grid-based localization method using RSS (received signal strength). To achieve high localization accuracy and low computational complexity, Gaussian mixture model is applied to approximate the signal distribution and a ROI (region of interest) is defined to limit the search region. The authors also discuss other techniques like AP selection and threshold control, which affects the localization accuracy. The experimental results indicate that an accuracy of 3m with 73.8% probability can be obtained in WiMap. Moreover, the running time is reduced greatly with limited ROI method.

scholarly journals MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1645 ◽  
Author(s):  
Ryota Kimoto ◽  
Shigemi Ishida ◽  
Takahiro Yamamoto ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Large Scale ◽  
Indoor Localization ◽  
Single Channel ◽  
Sensor Nodes ◽  
Localization Problem ◽  
Sensor Localization ◽  
Localization Accuracy ◽  
Localization System ◽  
Channel Diversity ◽  
Radio Signals

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.

scholarly journals RILS: RFID indoor localization system using mobile readers

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877128 ◽  
Author(s):  
Jinkai Liu ◽  
Yanqing Qiu ◽  
Kezhao Yin ◽  
Wentong Dong ◽  
Jiaqing Luo
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Large Scale ◽  
Indoor Localization ◽  
Scheduling Algorithm ◽  
Localization Accuracy ◽  
Localization System ◽  
Continuous Scanning ◽  
Reference Tags ◽  
Frequency Identification

The radio frequency identification technology was given greater interest as it is widely used for identification and localization in the cognitive radio sensor networks. While radio frequency identification–based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Using mobile readers mounted on guide rails, we design and implement an RFID indoor localization system, which requires neither reference tags nor received signal strength indicator functions, for stock-taking and searching in warehouse operations. In particular, we install two guide rails, which can allow a reader to move horizontally or vertically, on the ceiling of a warehouse or workshop. We then propose a continuous scanning algorithm to improve the accuracy for locating a single tagged object and a category-based scheduling algorithm to shorten the time for locating multiple tagged objects. Our primary experimental results show that RFID indoor localization system can achieve high time efficiency and localization accuracy in the indoor localization.

The WiMap

2011 ◽  
Vol 3 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Junjun Xu ◽  
Haiyong Luo ◽  
Fang Zhao ◽  
Rui Tao ◽  
Yiming Lin ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Signal Strength ◽  
Gaussian Mixture ◽  
Search Region ◽  
Threshold Control ◽  
Localization Accuracy ◽  
Signal Distribution ◽  
Localization System ◽  
The Internet Of Things ◽  
Grid Based

As positioning technology is an important foundation of the Internet of Things, a dynamic indoor WLAN localization system is proposed in this paper. This paper mainly concentrates on the design and implementation of the WiMap-a dynamic indoor WLAN localization system, which employs grid-based localization method using RSS (received signal strength). To achieve high localization accuracy and low computational complexity, Gaussian mixture model is applied to approximate the signal distribution and a ROI (region of interest) is defined to limit the search region. The authors also discuss other techniques like AP selection and threshold control, which affects the localization accuracy. The experimental results indicate that an accuracy of 3m with 73.8% probability can be obtained in WiMap. Moreover, the running time is reduced greatly with limited ROI method.

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