scholarly journals Fingerprint Database Variations for WiFi Positioning

2013 ◽  
Vol 11 (10) ◽  
pp. 3101-3107
Author(s):  
Nelson Acosta ◽  
Juan Toloza ◽  
Carlos Kornuta
Keyword(s):  
Mobile Device ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Relative Precision ◽  
Received Signal Strength Indicator ◽  
Positioning Systems ◽  
Fingerprinting Technique ◽  
Euclidian Distance ◽  
Fingerprint Database

Indoor positioning systems calculate the position of a mobile device (MD) in an enclosed environment with relative precision. Most systems use WiFi infrastructure and several positioning techniques, where the most commonly used parameter is RSSI (Received Signal Strength Indicator). In this paper, we analyze the fingerprinting technique to calculate the error window obtained with the Euclidian distance as main metric. Build variations are presented for the Fingerprint database analyzing various statistical values to compare the precision achieved with different indicators.

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.

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

scholarly journals A quadratic weighted centroid algorithm for tunnel personnel positioning

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091702
Author(s):  
Haiying Wang ◽  
Linhao Liang ◽  
Jian Xu ◽  
Hui She ◽  
Wuxiang Li
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Weighting Factor ◽  
Gaussian Filter ◽  
Received Signal Strength Indicator ◽  
Positioning Accuracy ◽  
Filter Model ◽  
Positioning Systems ◽  
Centroid Algorithm ◽  
Positioning Algorithm

To improve the accuracy and generalization of tunnel personnel positioning systems, this article proposes a quadratic weighted centroid algorithm. By adopting a Gaussian filter model to improve the range accuracy of the received signal strength indicator algorithm and combining the centroid algorithm and weighting factor with a trilateration positioning model, a quadratic weighted centroid algorithm is proposed to improve the positioning accuracy of unknown positioning nodes. The key ideas behind the quadratic weighted centroid algorithm include an optimization of the received signal strength indicator range value scheme, a centroid algorithm based on trilateral measurement positioning, and a weighting factor to improve the positioning accuracy of the trilateral centroid positioning algorithm. Compared with the centroid algorithm, the Min-Max multilateration algorithm, and the weighted centroid based on distance algorithm, the simulation results showed that (1) the positioning performance of the quadratic weighted centroid algorithm was superior to the other three algorithms; (2) when the reference nodes were symmetrically arranged, the positioning accuracy was higher than a fold line layout; and (3) when the lateral reference node spacing was extended from 20 to 30 m, the average positioning error met positioning accuracy requirements, which could reduce overall system costs.

scholarly journals A Review of Antennas for Indoor Positioning Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Luis Brás ◽  
Nuno Borges Carvalho ◽  
Pedro Pinho ◽  
Lukasz Kulas ◽  
Krzysztof Nyka
Keyword(s):  
Indoor Localization ◽  
Time Of Flight ◽  
Signal Strength ◽  
Direction Of Arrival ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Positioning Systems ◽  
Anchor Nodes ◽  
Localization Systems

This paper provides a review of antennas applied for indoor positioning or localization systems. The desired requirements of those antennas when integrated in anchor nodes (reference nodes) are discussed, according to different localization techniques and their performance. The described antennas will be subdivided into the following sections according to the nature of measurements: received signal strength (RSS), time of flight (ToF), and direction of arrival (DoA). This paper intends to provide a useful guide for antenna designers who are interested in developing suitable antennas for indoor localization systems.

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 Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1350 ◽  
Author(s):  
Sharareh Naghdi ◽  
Kyle O’Keefe
Keyword(s):  
Artificial Intelligence ◽  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Low Energy ◽  
Wireless Technology ◽  
Received Signal Strength Indicator ◽  
Bluetooth Low Energy ◽  
Office Environment

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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