scholarly journals Using Bluetooth to Implement a Pervasive Indoor Positioning System with Minimal Requirements at the Application Level

2012 ◽  
Vol 8 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Pedro J. Muñoz-Merino ◽  
Carlos Delgado Kloos
Keyword(s):  
Base Stations ◽  
Estimation Accuracy ◽  
Signal Quality ◽  
Positioning System ◽  
Reference Base ◽  
Related Data ◽  
Triangulation Method ◽  
Cellular Signal ◽  
The Cost ◽  
Indoor Positioning System

Different systems have been proposed to estimate the position of a mobile device using Bluetooth based on metrics such as the Radio Signal Strength Indicator (RSSI), the received Bit Error Rate (BER) or the Cellular Signal Quality (CSQ). These systems try to improve the estimation accuracy of the basic and straightforward triangulation method among discovered BT reference base stations at the cost of requiring that the positioning application has access to low level hardware related data (provided by the Host Controller Interface) and obtaining information which is in many cases hardware, and therefore device, dependent. In this paper we design, simulate, implement and validate a Bluetooth positioning system that only requires the ability to handle SDP service records at the application level, achieving mean errors around 1 to 3 meters, improving the basic triangulation method among discovered BT reference base stations.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals Semi-Autonomous Coordinate Configuration Technology of Base Stations in Indoor Positioning System Based on UWB

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Xiaofei Yang ◽  
Jun Wang ◽  
Hui Ye ◽  
Jianzhen Li
Keyword(s):  
Large Scale ◽  
Target Location ◽  
Wide Band ◽  
Indoor Positioning ◽  
Base Station ◽  
Base Stations ◽  
Positioning System ◽  
Linear Network ◽  
Gps System ◽  
Indoor Positioning System

In the global positioning system (GPS) denied environment, an indoor positioning system based on ultra-wide band (UWB) technology has been utilized for target location and navigation. It can provide a more accurate positioning measurement than those based on received signal strength (RSS). Although promising, it suffers from some shortcomings that base stations should be preinstalled to obtain reference coordinate information, just as navigation satellites in the GPS system. In order to improve the positioning accuracy, a large number of base stations should be preinstalled and assigned coordinates in the large-scale network. However, the coordinate setup process of the base stations is cumbersome, time consuming, and laborious. For a class of linear network topology, a semi-autonomous coordinate configuration technology of base stations is designed, which refers to three conceptions of segmentation, virtual triangle, and bidirectional calculation. It consists of two stages in every segment: Forward and backward. In the forward stage, it utilizes the manual coordinate setup method to deal with the foremost two base stations, and then the remaining base stations autonomously calculate their coordinates by building the virtual triangle train. In the backward stage, the reverse operation is performed, but the foremost two base stations of the next segment should be used as the head. In the last segment, the last two base stations should be used as the head. Integrating forward and backward data, the base stations could improve their location accuracy. It is shown that our algorithm is feasible and practical in simulation results and can dramatically reduce the system configuration time. In addition, the error and maximum base station number for one segment caused by our algorithm are discussed theoretically.

scholarly journals A high-accuracy indoor positioning system based on UWB

2018 ◽  
Vol 173 ◽  
pp. 01021
Author(s):  
Hanyu Liu ◽  
Yanhan Zeng ◽  
Ruguo Li ◽  
Huajie Huang
Keyword(s):  
Indoor Positioning ◽  
Mobile Node ◽  
High Accuracy ◽  
Base Stations ◽  
Ultra Wideband ◽  
Time Of Arrival ◽  
Positioning System ◽  
Wide Range ◽  
Fixed Base ◽  
Indoor Positioning System

In this paper, a high-accuracy indoor positioning system based on the ultra-wideband (UWB) technique is proposed. The proposed system uses a simple ranging process to obtain the distance between the mobile node and the fixed base stations. Besides, an improved time of arrival (ToA) algorithm with Kalman filtering is proposed to improve the positioning accuracy. Measurements have been performed in the real indoor 13m*7.6m environment with many obstacles and the root-mean-square error (RMSE) is less than 0.3m. The proposed system offers a wide range of application in robotics, industrial automation, post-sorting system and so on.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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