Using of GSM and Wi-Fi Signals for Indoor Positioning Based on Fingerprinting Algorithms (original) (raw)

Indoor Location Estimation Utilizing Wi-Fi Signals

International Journal of Emerging Trends in Engineering Research, 2020

Global Positioning System is commonly been used for locating a position of a specific structure in finding geographical coordinates of a target area. Though, this application is still having a restricted in term of the signals, might not well operated and ineffective for indoor usage. The study aim is to develop positioning and localization systems by using Wi-Fi signal. Estimation was made based on the measurement of wireless distance for estimation the user's coordinates. Analysis of views called the fingerprint algorithm is used in this study. The algorithm involved two phases over an offline and the online phases of the survey. Unidentified user's coordinates will be in the online phase by comparative databases collected in the survey phase. MATLAB Graphical User Interface and Android has been used to develop a user interface for simulation purposes. Several analyses were performed to define the precision and efficiency of occurred error as the number of access points and the traffic environment. Finally, the user required to provide several inputs e.g. the exact location and the RSS from AP's number at the present location. The simulation-based software will evaluate the estimation location and positioning of the user and will match to user's precise location.

Indoor Positioning Using the Modified Fingerprint Technique

2013

The Wi-Fi positioning systems available for enclosed spaces use the existing network infrastructure to calculate the position of the mobile device (MD). The most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the Fingerprint technique considering some variations aimed at improving the accuracy of the technique and minimizing calculation time. Significant field work is carried out, analyzing the accuracy achieved with each technique.

A new method for improving Wi-Fi-based indoor positioning accuracy

Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To ad-dress this problem, a new method for improving the indoor posi-tioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting meth-od to identify the initial position estimate of the smartphone us-er. Then two distance correction values in two roughly perpen-dicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

Indoor Positioning based on Wi-Fi Fingerprint Technique using Fuzzy K-Nearest Neighbor

Indoor positioning system based on Receive Signal Strength Indication(RSSI) from Wireless access equipment have become very popular in recent years. This system is very useful in many applications such as tracking service for older people, mobile robot localization and so on. While Outdoor environment using Global Navigation Satellite System(GNSS) and cellular network works well and widespread for navigator. However, there was a problem with signal propagation from satellites. They cannot be used effectively inside the building areas until a urban environment. In this paper we propose the Wi-Fi Fingerprint Technique using Fuzzy set theory to adaptive Basic K-Nearest Neighbor algorithm to classify the labels of a database system. It was able to improve the accuracy and robustness. The performance of our simple algorithm is evaluated by the experimental results which show that our proposed scheme can achieve a certain level of positioning system accuracy.

Gsm-Based Approach For Indoor Localization

2013

Ability of accurate and reliable location estimation in indoor environment is the key issue in developing great number of context aware applications and Location Based Services (LBS). Today, the most viable solution for localization is the Received Signal Strength (RSS) fingerprinting based approach using wireless local area network (WLAN). This paper presents two RSS fingerprinting based approaches – first we employ widely used WLAN based positioning as a reference system and then investigate the possibility of using GSM signals for positioning. To compare them, we developed a positioning system in real world environment, where realistic RSS measurements were collected. Multi-Layer Perceptron (MLP) neural network was used as the approximation function that maps RSS fingerprints and locations. Experimental results indicate advantage of WLAN based approach in the sense of lower localization error compared to GSM based approach, but GSM signal coverage by far outreaches WLAN coverage ...

Wireless Indoor Localization Using Fingerprinting Technique

Journal of Advanced Research in Dynamical and Control Systems, 2020

Indoor positioning has gained more interest as one of the upcoming applications due to its use in a variety of services. Multiple technologies such as Bluetooth, Wi-Fi, RFID. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing installed wireless infrastructures and good performances with low cost. With this study, we aim to provide a compromise between accurate positioning and feasibility of the system for practical applications. For this purpose, we minimize the fluctuations of Wi-Fi received signal strength (RSS) by filtering and we combine two approaches to locate a mobile user. At first, we implemeted the traditional fingerprinting technique that uses a real time matching of pre-recorded received signal strength (RSS) from the location data of the user transmitted to nearby access points (AP). Secondly, we used distance-based trilateration technique which determines positions using three known access points. The combination of the two methods provides enhancement of accuracy and wide indoor locating coverage. Regardless the locating data number, experiment confirmed a significant and a consistent performance in term of execution time and accuracy.

On the efficacy of WiFi indoor positioning in a practical setting

2013 IEEE Symposium on Computers and Communications (ISCC), 2013

We implement two popular WiFi, fingerprinting based indoor tracking mechanisms, namely the k-nearest neighbours and probabilistic positioning methods. Both mechanisms are evaluated in the context of an indoor position-tracking tablet application, following an investigation to determine optimal working parameters. Our results indicate that even after significant optimisation, both fingerprinting algorithms are highly sensitive to the location of the access points and do not produce finely grained location results. Although in this case the results are accurate enough for our purposes, factors such as the effect of natural body obstruction of the user as well as the location of the access points used in fingerprinting must be considered carefully if more accuracy is required.

An Enhanced Indoor Positioning Method Based on Wi-Fi RSS Fingerprinting

JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS, 2019

In WiFi-based indoor positioning, the received signal strength (RSS) measurements are commonly used to estimate the mobile user location. However, these measurements significantly fluctuate over time and are susceptible to human movement, multipath and Non-Line-of-Sight (NLOS) propagation, which reduce the location accuracy. In this paper, an enhanced positioning method based on the nearest neighbor algorithm is proposed. The set of the RSS samples recorded from several Access Points (APs) is used rather than their average, for reducing the location errors introduced by the RSS variations and the multipath problem. The proposed algorithm, named the Nearest K th Nearest Neighbor (NK-NN) is experimentally evaluated and compared to other powerful methods. The results show that the proposed method outperforms these methods.

Finding indoor position of person using wi-fi & smartphone:A survey

Positioning system can be used for different purposes and for different services, so a lot of research is going on to find a more accurate position with low error techniques with good results. The Positioning techniques have been actively studied recently due to service as well as safety and security matters. Global Positioning System (GPS) is more widely used for outdoor but GPS is not suitable for indoor . There are many localization systems with different architectures, configurations, accuracies and reliabilities Wi-Fi Positioning system (WPS) solves this problem. Here we find out position with the help of Wi-Fi signal strength. We also discuss location fingerprinting in detail since it is used in most current system or solutions. A small program installed on to calculate the position. This will help in many applications for mobile users and network administrators. It will make use of existing Wi-Fi infrastructure, although Wi-Fi system was never designed to find out the location. The smart device regularly scans the signal strengths for surrounding Wi-Fi access points and send information to a central server. This paper try to survey the recent work related to indoor positioning system.

The Novel Performance Evaluation Method of the Fingerprinting-Based Indoor Positioning

IEICE Transactions on Information and Systems, 2016

In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.