scholarly journals Accurate Indoor Localization Based on CSI and Visibility Graph

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2549 ◽  
Author(s):  
Zhefu Wu ◽  
Lei Jiang ◽  
Zhuangzhuang Jiang ◽  
Bin Chen ◽  
Kai Liu ◽  
...  
Keyword(s):  
Real World ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Visibility Graph ◽  
Localization Algorithm ◽  
Channel State ◽  
Critical Areas ◽  
Measuring Devices ◽  
Real World Applications ◽  
Better Than

Passive indoor localization techniques can have many important applications. They are nonintrusive and do not require users carrying measuring devices. Therefore, indoor localization techniques are widely used in many critical areas, such as security, logistics, healthcare, etc. However, because of the unpredictable indoor environment dynamics, the existing nonintrusive indoor localization techniques can be quite inaccurate, which greatly limits their real-world applications. To address those problems, in this work, we develop a channel state information (CSI) based indoor localization technique. Unlike the existing methods, we employ both the intra-subcarrier statistics features and the inter-subcarrier network features. Specifically, we make the following contributions: (1) we design a novel passive indoor localization algorithm which combines the statistics and network features; (2) we modify the visibility graph (VG) technique to build complex networks for the indoor localization applications; and (3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve about 96% accuracy on average and is more than 9% better than the state-of-the-art techniques.

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.

scholarly journals Damn You, Little h! (Or, Real-World Applications of the Hubble Constant Using Observed and Simulated Data)

2013 ◽  
Vol 30 ◽  
Author(s):  
Darren J. Croton
Keyword(s):  
Data Analysis ◽  
Galaxy Formation ◽  
Real World ◽  
Simulated Data ◽  
Theoretical Data ◽  
Hubble Constant ◽  
Uncertain Parameters ◽  
Modern Age ◽  
Real World Applications ◽  
Better Than

AbstractThe Hubble constant, H0, or its dimensionless equivalent, “little h”, is a fundamental cosmological property that is now known to an accuracy better than a few per cent. Despite its cosmological nature, little h commonly appears in the measured properties of individual galaxies. This can pose unique challenges for users of such data, particularly with survey data. In this paper we show how little h arises in the measurement of galaxies, how to compare like-properties from different datasets that have assumed different little h cosmologies, and how to fairly compare theoretical data with observed data, where little h can manifest in vastly different ways. This last point is particularly important when observations are used to calibrate galaxy formation models, as calibrating with the wrong (or no) little h can lead to disastrous results when the model is later converted to the correct h cosmology. We argue that in this modern age little h is an anachronism, being one of least uncertain parameters in astrophysics, and we propose that observers and theorists instead treat this uncertainty like any other. We conclude with a ‘cheat sheet’ of nine points that should be followed when dealing with little h in data analysis.

scholarly journals Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6972
Author(s):  
Harun Jamil ◽  
Faiza Qayyum ◽  
Faisal Jamil ◽  
Do-Hyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Sensor Data ◽  
Compensation Mechanism ◽  
Localization Algorithm ◽  
High Position ◽  
Zero Velocity ◽  
Conflicting Information

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

scholarly journals User Localization in an Indoor Environment by Combining Different Algorithms through Plurality Rule

2020 ◽  
pp. 64-68
Author(s):  
Muzaffer Cem Ates ◽  
Osman Emre Gumusoglu ◽  
Aslinur Colak ◽  
Nilgun Fescioglu Unver
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Electoral System ◽  
Plurality Rule ◽  
Accurate Information ◽  
Classification Algorithms ◽  
Accuracy Rate ◽  
Voting Rule ◽  
Accuracy Rates ◽  
Better Than

User localization in an indoor environment has a wide application area including production and service systems such as factories, smart homes, hospitals, nursing homes, etc. User localization based on Wi-Fi signals has been widely studied using various classification algorithms. In this type of problem, several Wi-Fi routers placed in an indoor environment provide signals with different strengths depending on the location/room of the user. Most classification algorithms successfully make the localization with a high accuracy rate. However, in the current literature, there is no widely accepted 'best' algorithm for solving this problem. This study proposes the use of the plurality rule to combine several classification algorithms and obtain a single result. Plurality voting rule is an electoral system where the candidate that polls the most vote wins the election. We apply the plurality rule to the indoor localization problem and generate the Majority algorithm. The Majority algorithm takes the 'votes' of five different classification algorithms and provides a single result through plurality rule. Results show that the mean accuracy rate of the Majority algorithm is higher than the classification algorithms it combines. In addition, we show that proving a single accuracy rate is not sufficient for declaring that an algorithm is better than the other. Classification algorithms select the training and test data randomly and different divisions result in different accuracy rates. In this study, we show that comparing the classification algorithms through confidence intervals provides more accurate information.

RFID Indoor Localization Algorithm Based on PSO

2012 ◽  
Vol 241-244 ◽  
pp. 972-975 ◽  
Author(s):  
Pei Zhi Wen ◽  
Ting Ting Su ◽  
Li Fang Li
Keyword(s):  
Indoor Environment ◽  
Lower Cost ◽  
Indoor Localization ◽  
Main Idea ◽  
Pso Algorithm ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Smoothing Filter ◽  
The Impact

In order to improve the positioning accuracy and reduce the localization cost, a kind of PSO-based RFID indoor localization algorithm is proposed in this paper. The main idea of this algorithm contains the following two aspects. First, due to the influence of none line of sight and multipath transmission in indoor environment, we adopt Gaussian Smoothing Filter to process Received Signal Strength Indicator (RSSI) values, which can reduce the impact of environmental factors on the position estimation effectively. Second, Particle of Swarm Optimization (PSO) algorithm is introduced to obtain a better positioning result. By experimenting in different indoor environment, the results demonstrate that the proposed approach can not only improve the precision of indoor localization, but has a lower cost and better robustness when compared to VIRE approach.

scholarly journals Endpoints-Clipping CSI Amplitude for SVM-Based Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3689 ◽  
Author(s):  
Zhanjun Hao ◽  
Yan Yan ◽  
Xiaochao Dang ◽  
Chenguang Shao
Keyword(s):  
Indoor Localization ◽  
Spatial Clustering ◽  
Support Vector ◽  
Localization Algorithm ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Inaccurate Data ◽  
Svm Algorithm ◽  
Fingerprint Database

With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique.

scholarly journals On the Generalization Power of Face and Gait in Gender Recognition

2014 ◽  
Vol 6 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yu Guan ◽  
Xingjie Wei ◽  
Chang-Tsun Li
Keyword(s):  
Real World ◽  
Recognition System ◽  
Gender Recognition ◽  
Human Face ◽  
Recognition Algorithms ◽  
Closed World ◽  
Real World Applications ◽  
Random Guess ◽  
Better Than

Human face/gait-based gender recognition has been intensively studied in the previous literatures, yet most of them are based on the same database. Although nearly perfect gender recognition rates can be achieved in the same face/gait dataset, they assume a closed-world and neglect the problems caused by dataset bias. Real-world human gender recognition system should be dataset-independent, i.e., it can be trained on one face/gait dataset and tested on another. In this paper, the authors test several popular face/gait-based gender recognition algorithms in a cross-dataset manner. The recognition rates decrease significantly and some of them are only slightly better than random guess. These observations suggest that the generalization power of conventional algorithms is less satisfied, and highlight the need for further research on face/gait-based gender recognition for real-world applications.

scholarly journals Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3431
Author(s):  
Mateusz Groth ◽  
Krzysztof Nyka ◽  
Lukasz Kulas
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Low Cost ◽  
Indoor Environments ◽  
Calibration Free ◽  
Architecture And Design ◽  
Positioning Algorithm ◽  
Parasitic Array ◽  
Better Than ◽  
Localization Systems

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

scholarly journals Distributed Pareto Optimization for Subset Selection

2018 ◽  
Author(s):  
Chao Qian ◽  
Guiying Li ◽  
Chao Feng ◽  
Ke Tang
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Subset Selection ◽  
Data Sets ◽  
Mapreduce Framework ◽  
Real World Data ◽  
Real World Applications ◽  
Approximation Guarantee ◽  
Better Than

The subset selection problem that selects a few items from a ground set arises in many applications such as maximum coverage, influence maximization, sparse regression, etc. The recently proposed POSS algorithm is a powerful approximation solver for this problem. However, POSS requires centralized access to the full ground set, and thus is impractical for large-scale real-world applications, where the ground set is too large to be stored on one single machine. In this paper, we propose a distributed version of POSS (DPOSS) with a bounded approximation guarantee. DPOSS can be easily implemented in the MapReduce framework. Our extensive experiments using Spark, on various real-world data sets with size ranging from thousands to millions, show that DPOSS can achieve competitive performance compared with the centralized POSS, and is almost always better than the state-of-the-art distributed greedy algorithm RandGreeDi.

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