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 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 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 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 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 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.

Localization and Map Building of Laser Sensor AGV Based on Kalman Filter

2013 ◽  
Vol 712-715 ◽  
pp. 1938-1943
Author(s):  
Li Xiao Guo ◽  
Fan Kun ◽  
Wen Jun Yan
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Laser Sensor ◽  
Map Building ◽  
Localization Accuracy ◽  
Key Technology ◽  
Navigation Algorithm ◽  
Automatic Guided Vehicle ◽  
Kalman Filter Algorithm ◽  
Navigation Method

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

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.

scholarly journals Ultra Wideband Assisted Localization of Semi-Autonomous Floor Scrubber

2017 ◽  
Vol 13 (2) ◽  
pp. 109
Author(s):  
Višeslav Čelan ◽  
Ivo Stančić ◽  
Josip Musić
Keyword(s):  
Ultra Wideband ◽  
Sensor Data ◽  
Line Of Sight ◽  
The Novel ◽  
Open Spaces ◽  
Localization Accuracy ◽  
Localization System ◽  
Advantages And Disadvantages ◽  
Ideal Line ◽  
Indoor Spaces

The paper describes the design and features of the novel semi-autonomous floor scrubber add-on module, used for cleaning large indoor spaces. Module is designed in such a manner that it can be easily attached and detached from scrubber machine and that additional sensors can be introduced if needed. The paper focuses on the localization capabilities of the machine in several sensor setups with emphasis on the use of ultra wideband (UWB) real-time localization system (RTLS). It also proposes fusion of sensor data from several sources including novel use of wheel encoder’s data in UWB setup. Analysis is performed in terms of localization accuracy and reliability as well as associated advantages and disadvantages. Obtained results demonstrated that inclusion of UWB subsystem, despite its price and accuracy (20 cm in ideal, line of sight, conditions), based on behavior switching yields more reliable and accurate results in open spaces (up to 25 times in position and 2 times in orientation) and that its accuracy can be further improved with inclusion of wheel encoder data.

WiCLoc: A Novel CSI-based Fingerprint Localization System

2019 ◽  
Vol 34 (04) ◽  
pp. 2058003
Author(s):  
Lei Zhang ◽  
Yanjun Hu ◽  
Yafeng Liu ◽  
Jiaxiang Li ◽  
Enjie Ding
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Smart Devices ◽  
Fingerprint Matching ◽  
Wifi Networks ◽  
Localization System ◽  
Exhibition Hall ◽  
Localization Systems

With the rapid development of smart devices and WiFi networks, WiFi-based indoor localization is becoming increasingly important in location-based services. Among various localization techniques, the fingerprint-based method has attracted much interest due to its high accuracy and low equipment requirement. Traditional fingerprint-based indoor localization systems mostly obtain positioning by measuring the received signal strength indicator (RSSI). However, the RSSI is affected by environmental influences, thereby limiting the precision of positioning. Therefore, we propose a new indoor fingerprint localization system based on channel state information (CSI). We adopt a novel method, in which the amplitude and phase of the CSI are fused to generate fingerprints in the training phase and apply a weighted [Formula: see text]-nearest neighbor (KNN) algorithm for fingerprint matching during the estimation phase. The system is validated in an exhibition hall and laboratory and we also compare the results of the proposed system with those of two CSI-based and an RSSI-based fingerprint localization systems. The results show that the proposed system achieves a minimum mean distance error of 0.85[Formula: see text]m in the exhibition hall and 1.28[Formula: see text]m in the laboratory, outperforming the other systems.

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