scholarly journals Effect of Sample Sizes in Fingerprinting Database for Wi-Fi System

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ahmad Hakimi Bin Ahmad Sa'ahiry ◽  
Abdul Halim Ismail ◽  
Latifah Munirah Kamaruddin ◽  
Mohd Sani Mohamad Hashim ◽  
Muhamad Safwan Muhamad Azmi ◽  
...  
Keyword(s):  
Sample Size ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Shadow Fading ◽  
Sample Sizes ◽  
K Nearest Neighbor ◽  
Fingerprinting Method ◽  
Indoor Positioning System

Indoor positioning system has been an essential work to substitute the Global Positioning System (GPS). GPS utilizing Global Navigation Satellite Systems (GNSS) cannot provide an accurate positioning in the indoor due to the multipath effect and shadow fading. Fingerprinting method with Wi-Fi technology is a promising system to solve this issue. However, there are several problems with the fingerprinting method. The fingerprinting database collected has different sample sizes where the previous researcher does not indicate any standard for the sample size to be used. In this paper, the effect of the sample sizes in fingerprinting database for Wi-Fi technology has been discussed deeply. The statistical analyzation for different sample sizes has been analyzed. Furthermore, two methods which are K- Nearest Neighbor (KNN) and Deep Neural Network (DNN) are being used to examine the effect of the sample sizes in term of accuracy and distance error. The discussion in this paper will contribute to the better sample size selection depending on the method taken by the user. The result shows that sample sizes are an important metrics in developing the indoor positioning system as it effects the result of the location estimation.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

A New Indoor Positioning System Using Artificial Encoded Magnetic Fields

2017 ◽  
Vol 71 (2) ◽  
pp. 299-316 ◽  
Author(s):  
Falin Wu ◽  
Yuan Liang ◽  
Yong Fu ◽  
Chenghao Geng
Keyword(s):  
Magnetic Fields ◽  
Three Dimensional ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Indoor Environments ◽  
Step Method ◽  
Satellite Systems ◽  
Multipath Effects ◽  
Indoor Positioning System

The demand for accurate indoor positioning continues to grow but the predominant positioning technologies such as Global Navigation Satellite Systems (GNSS) are not suitable for indoor environments due to multipath effects and Non-Line-Of-Sight (NLOS) conditions. This paper presents a new indoor positioning system using artificial encoded magnetic fields, which has good properties for NLOS conditions and fewer multipath effects. The encoded magnetic fields are generated by multiple beacons; each beacon periodically generates unique magnetic field sequences, which consist of a gold code sequence and a beacon location sequence. The position of an object can be determined with measurements from a tri-axial magnetometer using a three-step method: performing time synchronisation between sensor and beacons, identifying the beacon field and the beacon location, and estimating the position of the object. The results of the simulation and experiment show that the proposed system is capable of achieving Two-Dimensional (2D) and Three-Dimensional (3D) accuracy at sub-decimetre and decimetre levels, respectively.

scholarly journals Platform and Algorithm Development for a RFID-Based Indoor Positioning System

2014 ◽  
Vol 02 (03) ◽  
pp. 279-291 ◽  
Author(s):  
Han Zou ◽  
Lihua Xie ◽  
Qing-Shan Jia ◽  
Hengtao Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Support Vector ◽  
Rfid Tags ◽  
Positioning System ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Reference Tags ◽  
Indoor Positioning System

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

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