RADIO PROPAGATION CHARACTERISTICS OF INDOOR LOCATION SYSTEM BASED ON RSSI AT 490 MHz

2014 ◽  
Vol 23 (07) ◽  
pp. 1450094 ◽  
Author(s):  
WEIHONG FAN ◽  
MAJID AHMADI ◽  
FENG XUE
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Propagation Characteristic ◽  
Radio Propagation ◽  
Test Results ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Localization And Tracking

Localization and tracking technology based on received signal strength indicator (RSSI) is one of the most popular topics because of its low demand on hardware and cost. But the complexity of the indoor environment, leads to the uncertainty of the radio propagation which can seriously affect the positioning accuracy based on the received signal strength. Focused on the wall reflection in the indoor environment, the radio propagation characteristic based on ray-tracing model is analyzed and one strategy for the near wall localization is presented. The actual hardware platform and experimental test results show the applicability of the empirical logarithmic path loss model for localization and the effect of the wall reflection.

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 Estimation of the number of people in an indoor environment based onWiFi received signal strength indicator

2021 ◽  
Vol 10 (3) ◽  
pp. 1475-1483
Author(s):  
Hakam Marwan Zaidan ◽  
Emad Ahmed Mohammed ◽  
Dheyaa Hussein Alhelal
Keyword(s):  
Indoor Environment ◽  
Polynomial Regression ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Concrete Wall ◽  
Received Signal Strength Indicator ◽  
The People ◽  
Two Stages ◽  
Non Line Of Sight

WiFi access points are widely spread everywhere in all our daily life routines. Using these devices to provide services other than the Internet is becoming familiar nowadays.This paper conducts an experimental study to estimate the number of people in an indoor environment through two system setups, line of sight, and non-line of sight. Relationship modeling between WiFi received signal and the number of people uses polynomial regression. The experiment comprised of two stages: first is the data collection from a controlled number of people. Then, the collected data used to train the system through polynomial regression. The second is testing the system’s effectiveness by applying it to an uncontrolled environment. Testing results revealed efficiency in using WiFi received signal strength to do the people counting (up to 60) because of the accuracy achievements of 93.17% in the line of sight system. The non-line of sight system disclosed randomness in the received signal strength indicator regardless of the change in the number of people. The  randomness is mainly caused by the fading effect of the concrete wall. Therefore it is inefficient to use the non-line of sight system in concrete buildings.

scholarly journals Analisis Performa Bluetooth pada Sistem Alarm Pengingat Barang yang Tertinggal dengan Smartphone Android

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Rafiqmia Khairuddin Nur Hammam ◽  
Hidayat Nur Isnianto ◽  
Sri Lestari ◽  
Y. Wahyo Setiyono
Keyword(s):  
Packet Loss ◽  
Signal Strength ◽  
Smartphone Application ◽  
Received Signal Strength ◽  
Line Of Sight ◽  
Test Results ◽  
Received Signal Strength Indicator ◽  
Measured Distance ◽  
Receiver Module ◽  
Point To Point

Someone sometimes forgets to put their belongings so that they leave them somewhere, it will pose a risk of losing their belongings. To solve this problem, a reminder is needed so that it is expected to minimize the risk of loss. This device implements point-to-point communication from the Bluetooth Low Energy AT-09 transmitter module with Arduino Nano and the receiver module with an Android smartphone. This reminder device will activate an alarm on the Android smartphone application when the two modules are more than 5 meters away. The measured distance is converted from the Received Signal Strength Indicator (RSSI) value received by the smartphone which is affected by obstructions, packet loss, and delay. Based on the test results, at a distance of 5 meters, the system can be connected and work properly, in unobstructed conditions (Line of Sight) or obstructed conditions (Non-Line of Sight). In the blocked condition, it has a packet loss of 1.1% to 4.4%, the received signal strength (RSSI) has decreased the value to a difference of -8 dBm, and the delay time is 2 seconds.

An Improved RSSI Location Algorithm Suitable for Digital Workshop

2014 ◽  
Vol 945-949 ◽  
pp. 3444-3449
Author(s):  
Yun Feng Leng ◽  
Hai Ping Zhu ◽  
Fei He
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Test Results ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Automatic Guided Vehicles ◽  
Location Algorithm ◽  
Application Requirements

Relying on the background of constructing digital workshop, an improved RSSI (Received Signal Strength Indicator) location algorithm is introduced in this paper. At first, the application requirements of RSSI location algorithm are discussed from cargo tracking, instrument management and automatic guided vehicles three aspects; and then focuses on the improvements of the new RSSI location algorithm. The test results and analysis show that the positioning accuracy up to 0.89 meter by applying the new algorithm, and it can meet positioning requirements of digital workshop.

scholarly journals Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3419 ◽  
Author(s):  
Yitang Peng ◽  
Xiaoji Niu ◽  
Jian Tang ◽  
Dazhi Mao ◽  
Chuang Qian
Keyword(s):  
Indoor Environment ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Kriging Interpolation ◽  
Unmanned Ground Vehicle ◽  
Received Signal Strength Indicator ◽  
Ground Vehicle ◽  
Fingerprint Database ◽  
Signals Of Opportunity

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

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