scholarly journals PEOPLE’S PPRESENCE EFFECT ON WLAN-BASED IPS’ ACCURACY

2015 ◽  
Vol 77 (9) ◽  
Author(s):  
Iyad H Alshami ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin
Keyword(s):  
Low Cost ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Positioning System ◽  
Indoor Environments ◽  
Received Signal Strength Indicator ◽  
Indoor Positioning System ◽  
Closed Environment ◽  
Presence Of People

In order to enable Location Based Service (LBS) closed environment, many technologies have been investigated to replace the Global Positioning System (GPS) in the localization process in indoor environments. WLAN is considered as the most suitable and powerful technology for Indoor Positioning System (IPS) due to its widespread coverage and low cost. Although WLAN Received Signal Strength Indicator (RSS) fingerprinting can be considered as the most accurate IPS method, this accuracy can be weakened due to WLAN RSS fluctuation. WLAN RSS fluctuates due to the multipath being influenced by obstacles presence. People presence under WLAN coverage can be considered as one of the main obstacles which can affect the WLAN-IPS accuracy. This research presents experimental results demonstrating that people’s presence between access point (AP) and mobile device (MD) reduces the received signal strength by -2dBm to -5dBm. This reduction in RSS can lead to distance error greater than or equal to 2m. Hence, any accurate IPS must consider the presence of people in the indoor environment. 

scholarly journals DL-IPS: Deep Learning Based Indoor Positioning System for Improved Accuracy

2019 ◽  
Vol 8 (4) ◽  
pp. 10797-10801
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Signal Strength ◽  
Low Energy ◽  
Positioning System ◽  
Received Signal Strength Indicator ◽  
Indoor Tracking ◽  
And Control ◽  
Improved Accuracy ◽  
Indoor Positioning System

Indoor tracking has evolved with various methods and well known these days. There are diverse types of solutions that concentrate on exactness, low cost, and control utilization within the field. Particularly in recent years, Received Signal Strength Indicator based positioning estimation have been getting popular. Still, the accuracy are not adequate, and there's no correct way chosen to overcome this issue. In this paper, we propose a strategy that leverage Deep Learning and Wi-Fi/BLE (Bluetooth Low Energy) Fingerprinting strategy to produce superior precise accuracy.

scholarly journals Indoor localization system based on virtual access points with filtering schemes

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986613 ◽  
Author(s):  
Dong Myung Lee ◽  
Boney Labinghisa
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Performance Testing ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Indoor Environments ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Error Distance

In indoor positioning techniques, Wi-Fi is one of the most used technology because of its availability and cost-effectiveness. Access points are usually the main source of Wi-Fi signals in an indoor environment. If access points are optimized to cover the indoor area, this could improve Wi-Fi signal distribution. This article proposed an alternative to optimizing access point placement and distribution by introducing virtual access points that can be virtually placed in any part of the indoor environment without installation of actual access points. Virtual access points will be created heuristically by correlating received signal strength indicator of already existing access points and through linear regression. After introducing virtual access points in the indoor environment, next will be the addition of filters to improve signal fluctuation and reduce noise interference. Kalman filter has been previously used together with virtual access point and showed improvement by decreasing error distance of Wi-Fi fingerprinting results. This article also aims to include particle filter in the system to further improve localization and test its effectiveness when paired with Kalman filter. The performance testing of the algorithm in different indoor environments resulted in 3.18 and 3.59 m error distances. An improvement was added on the system by using relative distances instead of received signal strength indicator values in distance estimation and gave an error distance average of 1.85 m.

scholarly journals Indoor Positioning System (IPS) Berdasarkan Kekuatan Received Signal Strength Indicator (RSSI)

2018 ◽  
Vol 2 (01) ◽  
pp. 27-33
Author(s):  
Budi Rahmadya Rahmadya
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Shopping Mall ◽  
Positioning System ◽  
Received Signal Strength Indicator ◽  
Indoor Positioning System

Shopping Mall merupakan area pusat perbelanjaan yang besar dan memiliki sistem keamanan seperti sistem layanan informasi yang dapat dimanfaatkan oleh konsumen untuk mendapatkan informasi yang dibutuhkan. Penggunaan sistem layanan informasi pada area Shopping Mall bagi konsumen terkadang sangat tidak efektif. Hal ini dikarenakan konsumen membutuhkan waktu yang lama dalam mendapatkan informasi, dimana konsumen terlebih dahulu harus mencari lokasi tempat sistem layanan informasi tersebut. Hal ini menjadikan sistem keamanan pada Shopping Mall menjadi lemah. Indoor Positioning System (IPS) merupakan sistem yang dapat digunakan untuk mengetahui posisi pengguna melalui kekuatan sinyal Wi-Fi yang didapat dalam gedung. Pada penelitian ini, penulis membuat suatu aplikasi android yang dapat digunakan untuk mengetahui posisi konsumen pada area Shopping Mall tersebut.

scholarly journals An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

2020 ◽  
Vol 10 (1) ◽  
pp. 117-123
Author(s):  
Bhulakshmi Bonthu ◽  
M Subaji
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Positioning System ◽  
Radio Waves ◽  
Direct Path ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Measuring Techniques ◽  
Indoor Positioning System

AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

scholarly journals Research and Application of Underground WLAN Adaptive Radio Fingerprint Database

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1182
Author(s):  
Jiansheng Qian ◽  
Mingzhi Song
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Reference Points ◽  
Normal Operation ◽  
Positioning System ◽  
Widespread Application ◽  
Radio Fingerprint ◽  
Fingerprint Database ◽  
User Location

Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization–user-location trajectory feedback (QPSO–ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user’s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO–ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO–ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system.

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).

Theory and Practice of Signal Strength-Based Localization in Indoor Environments

2009 ◽  
pp. 282-301
Author(s):  
Eiman Elnahrawy ◽  
Richard P. Martin
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Theory And Practice ◽  
Location Information ◽  
Positioning System ◽  
Indoor Environments ◽  
Sensor Applications ◽  
Strength Based

This chapter discusses radio-based positioning. It surveys and compares several received signal strength localization approaches from two broad categories: point-based and area-based. It also explores their performance and means to improve it. It describes GRAIL - a sample positioning system. It finally concludes with a brief discussion of sensor applications that utilize location information.

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