scholarly journals A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sharly Joana Halder ◽  
Wooju Kim
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Link Quality ◽  
Average Error ◽  
Indoor Environments ◽  
Interference Avoidance ◽  
Localization System ◽  
Context Aware Computing ◽  
Link Quality Indicator ◽  
Unknown Node

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

scholarly journals IMPROVED INDOOR LOCALIZATION WITH DIVERSITY AND FILTERING BASED ON RECEIVED SIGNAL STRENGTH MEASUREMENTS

2010 ◽  
pp. 9-15
Author(s):  
Andreas Fink ◽  
Helmut Beikirch ◽  
Matthias Voss
Keyword(s):  
Data Transmission ◽  
Indoor Localization ◽  
Estimation Error ◽  
Path Loss ◽  
Distance Estimation ◽  
Signal Strength ◽  
Location Estimation ◽  
Indoor Environments ◽  
Redundant Data ◽  
Unknown Node

Distance estimation by the evaluation of RSSI measurements is a simple and well-known technique to predict the position of an unknown node. Therefore the infrastructure does not have to be extended by expensive hardware for synchronization or direction approximation. However, with the localization based on RSSI measurements common and proven systems can be used for the infrastructure. For indoor environments the distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. If also space diversity and plausibility filtering are used, the Location Estimation Error can be reduced significantly. The investigations show that a good performance for precision and availability can also be reached with low infrastructural costs.

scholarly journals Improving Indoor Localization System Using a Partitioning Technique Based on RSS and ToA

2021 ◽  
Vol 3 (1) ◽  
pp. 47-54
Author(s):  
Mahmood F . Mosleh ◽  
Faeza A. Abed ◽  
Zahraa Abbas Hamza
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Average Error ◽  
Localization System ◽  
The Real ◽  
Special Values ◽  
Case Study Area ◽  
Partitioning Technique ◽  
Interference Problems

Designing a localization system for an indoor environment faces more challenges because of multipath and interference problems. In this field, the most important techniques used for such environment, are RSS and ToA which need to be improved especially from more interference because of the huge multipath problems. In this paper, a case study of a selected building is chosen in order to apply the proposed technique of this research. Such proposal is based on the PT ‎ of the area in the case study into MZ. Each zone is allocated special values for the parameters used to estimate the target positions. WI package is used to simulate the case study area and apply such proposal based on RSS and ToA. The results confirm that the estimated locations are close to the real locations by the average error of (2.8) meter and (0.192) meter for ToA corresponding one zone and four zones ‎respectively. ‎ In contrast, the results of our experiment show that the accuracy is improved from an average error of (2.4) meter and (0.217) meter for RSS corresponding one zone and four zones ‎respectively‎. Such results confirm that dividing the case study area into more zones leads to more accuracy.

scholarly journals An Analysis of Device-Free and Device-Based WiFi-Localization Systems

2014 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Heba Aly ◽  
Moustafa Youssef
Keyword(s):  
Indoor Localization ◽  
Indoor Environments ◽  
Factors Affecting ◽  
Localization System ◽  
Finger Print ◽  
Area Of Interest ◽  
Crowd Effect ◽  
Wifi Localization ◽  
Technology Upgrade ◽  
Device Free

WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points (AP) mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes of WiFi-based localization: device-based and device-free. The authors analysis highlights factors affecting the localization system accuracy, how to tune it for better localization, and provides insights for both researchers and practitioners.

scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals Tools for modelling distance estimation based on RSSI

2020 ◽  
Author(s):  
D. Dobrilovic ◽  
Z. Stojanov ◽  
J. Stojanov ◽  
M. Malic
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Signal Strength ◽  
Software Tools ◽  
Sensor Nodes ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Microsoft Access ◽  
Main Challenge ◽  
Satellite Navigation Systems

The systems for localization of resources in indoor environments based on Received Signal Strength Indicator (RSSI) are widely used today. Since satellite navigation systems, such as GPS or GLONASS, have certain difficulties in the indoor environments, the signals of deployed wireless devices, such as sensor nodes, access points etc, are used for localization instead. Those systems are known as Indoor Positioning System (IPS). Those systems are used for resource tracking and positioning in places such as airports, railway stations, shopping malls, warehouses, production facilities, construction sites, and healthcare institutions. The Bluetooth Low Energy is one of the wireless technologies that can be used with great efficiency for indoor localization. It offers easy and economic implementation on mobile devices such as smart phones and tablets. There are many techniques used for determination of position. In a number of methods, such as ROCRSSI or MinMax, the distance from the wireless nodes is used for calculating the location. In those systems the main challenge is to accurately estimate distance from the device based on signal strength. In this paper, usability of various software tools for modelling the distance estimation based on RSSI is discussed. Those software tools are Microsoft Access, R Studio, Octave, and Python.

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

scholarly journals Towards human distance estimation using a thermal sensor array

2021 ◽  
Author(s):  
Abdallah Naser ◽  
Ahmad Lotfi ◽  
Joni Zhong
Keyword(s):  
High Resolution ◽  
Independent Living ◽  
Sensor Array ◽  
Distance Estimation ◽  
Thermal Sensor ◽  
Indoor Environments ◽  
Contagious Diseases ◽  
Novel Approach ◽  
Thermal Cameras ◽  
Distance Estimator

AbstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2  m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

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.

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