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.

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 Accurate indoor positioning system based on modify nearest point technique

2022 ◽  
Vol 12 (2) ◽  
pp. 1593
Author(s):  
Omar Ibrahim Mustafa ◽  
Hawraa Lateef Joey ◽  
Noor Abd AlSalam ◽  
Ibrahim Zeghaiton Chaloob
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Real Data ◽  
Reference Points ◽  
Positioning System ◽  
Indoor Environments ◽  
Nearest Point ◽  
The Impact ◽  
Indoor Positioning System

Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).

scholarly journals INDOOR LIDAR RELOCALIZATION BASED ON DEEP LEARNING USING A 3D MODEL

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 541-547
Author(s):  
H. Zhao ◽  
D. Acharya ◽  
M. Tomko ◽  
K. Khoshelham
Keyword(s):  
Indoor Environment ◽  
3D Model ◽  
Indoor Localization ◽  
High Accuracy ◽  
Lidar Data ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Range Image ◽  
Range Images ◽  
Mapping Systems

Abstract. Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for lidar sensors in indoor environments to estimate the initial lidar pose using a CNN pose regression network trained using a 3D model. A set of synthetic lidar frames are generated from the 3D model with known poses. Each lidar range image is a one-channel range image, used to train the CNN pose regression network from scratch to predict the initial sensor location and orientation. The CNN regression network trained by synthetic range images is used to estimate the poses of the lidar using real range images captured in the indoor environment. The results show that the proposed CNN regression network can learn from synthetic lidar data and estimate the pose of real lidar data with an accuracy of 1.9 m and 8.7 degrees.

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 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.

Performance Comparison Based-RSSI for Sensor Networks in Different Environments

2014 ◽  
Vol 989-994 ◽  
pp. 4547-4550 ◽  
Author(s):  
Yan Feng ◽  
Bo Yi
Keyword(s):  
Distance Estimation ◽  
Performance Comparison ◽  
Wireless Channel ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Indoor Environments ◽  
Power Efficient ◽  
Network Applications ◽  
Cost Efficient ◽  
Repeated Experiment

A lot of the wireless sensor network applications call for sensor nodes to be applicable to any environment. RSSI-based measurements implement conveniently and are cost-efficient and power-efficient in practice. This paper compares the distance estimation performances based RSSI in three different environments by experiments and discusses their characteristic in detail. We carry out a large amount of repeated experiment based on RSSI in three kinds of environments over the 2.4GHz wireless channel. Subsequently, we focus on studying and discussing data analyses of RSSI and possible improvements in each environment. Experimental results demonstrate availability and feasibility of RSSI in outdoor and indoor environments.

scholarly journals An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Yasmine Rezgui ◽  
Ling Pei ◽  
Xin Chen ◽  
Fei Wen ◽  
Chen Han
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Shopping Mall ◽  
Support Vector ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Wifi Localization ◽  
Signal Fluctuation ◽  
Strength Difference ◽  
Correct Estimation

This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

scholarly journals Enhance RSS-Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Peng Xiang ◽  
Peng Ji ◽  
Dian Zhang
Keyword(s):  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Indoor Environments ◽  
Wireless Devices ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Localization Method ◽  
Physical Features ◽  
Additional Hardware

Indoor localization technologies based on Radio Signal Strength (RSS) attract many researchers’ attentions, since RSS can be easily obtained by wireless devices without additional hardware. However, such technologies are apt to be affected by indoor environments and multipath phenomenon. Thus, the accuracy is very difficult to improve. In this paper, we put forward a method, which is able to leverage various other resources in localization. Besides the traditional RSS information, the environmental physical features, e.g., the light, temperature, and humidity information, are all utilized for localization. After building a comprehensive fingerprint map for the above information, we propose an algorithm to localize the target based on Naïve Bayesian. Experimental results show that the successful positioning accuracy can dramatically outperform traditional pure RSS-based indoor localization method by about 39%. Our method has the potential to improve all the radio frequency (RF) based localization approaches.

scholarly journals RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 235-247 ◽  
Author(s):  
C. Guney
Keyword(s):  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Indoor Location ◽  
High Level

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

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