Indoor location using received signal strength of IEEE 802.11b access point

2006 ◽  
Author(s):  
G.I. Wassi ◽  
C. Despins ◽  
D. Grenier ◽  
C. Nerguizian
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Ieee 802.11B ◽  
Indoor Location

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

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 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 Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1828
Author(s):  
Marcin Luckner ◽  
Rafał Górak
Keyword(s):  
Quality Of Service ◽  
Dynamic System ◽  
Automatic Detection ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Strength Characteristics ◽  
The Mean ◽  
Indoor Localisation

This paper faces the issue of changing the received signal strength (RSS) from an observed access point (AP). Such a change can reduce the Quality of Service (QoS) of a Wi-Fi-based Indoor Localisation System. We have proposed a dynamic system based on an estimator of RSS using the readings from other APs. Using an optimal threshold, the algorithm recognises an AP that has changed its characteristics. Next, the system rebuilds the localisation model excluding the changed AP to keep QoS. For the tests, we simulated a change in the analysed Wi-Fi network by replacing the measured RSS by an RSS obtained from the same AP model that lies in another place inside the same multi-floor building. The algorithm was evaluated in simulations of an isolated single-floor building, a single-floor building and a multi-floor building. The mean increase of the localisation error obtained by the system varies from 0.25 to 0.61 m after the RSS changes, whereas the error increase without using the system is between 1.21 and 1.98 m. The system can be applied to any service based on a Wi-Fi network for various kinds of changes like a reconfiguration of the network, a local malfunction or ageing of the infrastructure.

scholarly journals Fusion of Channel State Information and Received Signal Strength for Indoor Localization Using a Single Access Point

2020 ◽  
Vol 12 (12) ◽  
pp. 1995
Author(s):  
David Sánchez-Rodríguez ◽  
Miguel A. Quintana-Suárez ◽  
Itziar Alonso-González ◽  
Carlos Ley-Bosch ◽  
Javier J. Sánchez-Medina
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Channel State ◽  
State Information ◽  
Single Access ◽  
5 Ghz

In recent years, indoor localization systems based on fingerprinting have had significant advances yielding high accuracies. Those approaches often use information about channel communication, such as channel state information (CSI) and received signal strength (RSS). Nevertheless, these features have always been employed separately. Although CSI provides more fine-grained physical layer information than RSS, in this manuscript, a methodology for indoor localization fusing both features from a single access point is proposed to provide a better accuracy. In addition, CSI amplitude information is processed to remove high variability information that can negatively influence location estimation. The methodology was implemented and validated in two scenarios using a single access point located in two different positions and configured in 2.4 and 5 GHz frequency bands. The experiments show that the methodology yields an average error distance of about 0.1 m using the 5 GHz band and a single access point.

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