Mobile Positioning System using Signal Strength Measurement for WCDMA System

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

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Trilateration Method For Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol

JURNAL INFOTEL ◽

10.20895/infotel.v12i1.380 ◽

2020 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Herryawan Pujiharsono ◽

Duwi Utami ◽

Rafina Destiarti Ainul

Keyword(s):

Path Loss ◽

Signal Strength ◽

Indoor Positioning ◽

Positioning System ◽

Observation Area ◽

Current Location ◽

Zigbee Protocol ◽

The Mean ◽

Unknown Node ◽

Indoor Positioning System

Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area is 0.9836 and the Mean Square Error of the test is 1.251 meters, which indicates that the system can be a solution to the indoor GPS problem.

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Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments

Data ◽

10.3390/data5030067 ◽

2020 ◽

Vol 5 (3) ◽

pp. 67 ◽

Cited By ~ 2

Author(s):

Fernando J. Aranda ◽

Felipe Parralejo ◽

Fernando J. Álvarez ◽

Joaquín Torres-Sospedra

Keyword(s):

Signal Strength ◽

Received Signal Strength ◽

Low Energy ◽

Location Based Services ◽

Positioning System ◽

Positioning Accuracy ◽

Bluetooth Low Energy ◽

Outdoor Environments ◽

The University

The technologies and sensors embedded in smartphones have contributed to the spread of disruptive applications built on top of Location Based Services (LBSs). Among them, Bluetooth Low Energy (BLE) has been widely adopted for proximity and localization, as it is a simple but efficient positioning technology. This article presents a database of received signal strength measurements (RSSIs) on BLE signals in a real positioning system. The system was deployed on two buildings belonging to the campus of the University of Extremadura in Badajoz. the database is divided into three different deployments, changing in each of them the number of measurement points and the configuration of the BLE beacons. the beacons used in this work can broadcast up to six emission slots simultaneously. Fingerprinting positioning experiments are presented in this work using multiple slots, improving positioning accuracy when compared with the traditional single slot approach.

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Research and Application of Underground WLAN Adaptive Radio Fingerprint Database

Sensors ◽

10.3390/s20041182 ◽

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.

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Analysis of a signal strength based positioning system for commercial environments

Second IEEE Consumer Communications and Networking Conference, 2005. CCNC. 2005 ◽

10.1109/ccnc.2005.1405229 ◽

2005 ◽

Cited By ~ 1

Author(s):

Jessica S. King Michael J. Johnson

Keyword(s):

Signal Strength ◽

Positioning System ◽

Strength Based

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

Jurnal Teknologi ◽

10.11113/jt.v77.6203 ◽

2015 ◽

Vol 77 (9) ◽

Cited By ~ 2

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.

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Forecast of Thunderstorm Cloud Trend Based on Monitoring Data of Thunder Mobile Positioning System

Mobile Information Systems ◽

10.1155/2021/8062549 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Mingfa Wu ◽

Chongjing Yang ◽

Xiangke Liu

Keyword(s):

Lightning Discharge ◽

Monitoring Data ◽

Practical Significance ◽

Lightning Strike ◽

Natural Phenomenon ◽

Main Task ◽

Positioning System ◽

Lightning Strikes ◽

Thunderstorm Cloud ◽

Mobile Positioning

As a natural phenomenon, thunder and lightning have a major impact on human production and life. As an important part of lightning protection technology, the main task of the lightning mobile positioning system is to detect and determine the location of lightning and, at the same time, provide more accurate lightning discharge parameters for lightning research. It is a new technology that serves the entire society and is in urgent need of development. This paper aims to study the trend prediction of thunderstorm cloud based on the monitoring data of the thunder and lightning mobile positioning system. In this thesis, the inverse distance-weighted interpolation method can be used to determine the lightning area and the principle of lightning monitoring and positioning, and the classification of lightning and the practical significance of lightning mobile positioning system monitoring are also studied. Finally, the Hurst index of this paper can reveal the trend elements in the time series well through the experiment, so as to judge the lightning strikes. At the same time, it also introduces everyone’s satisfaction survey on the lightning mobile positioning system. The results of this paper show that the lightning mobile positioning system has been widely used in our country’s meteorological monitoring stations, and it plays a very important role in our national defense lightning strikes and effectively realizes the lightning strike prediction in the monitoring process, which can better enable the competent department to take timely and accurate measures to prevent lightning strikes. Experimental analysis shows that the accuracy of the lightning mobile positioning system has reached 92%, and the practicability has reached 88%.

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