scholarly journals Modified fingerprinting localization technique of indoor positioning system based on coordinates

2019 ◽  
Vol 15 (3) ◽  
pp. 1345
Author(s):  
Rhowel M. Dellosa ◽  
Arnel C. Fajardo ◽  
Ruji P. Medina
Keyword(s):  
Radio Frequency ◽  
Long Range ◽  
Indoor Environment ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Location Estimation ◽  
Positioning System ◽  
Negative Effects ◽  
Localization Technique ◽  
Indoor Positioning System

<span>The fingerprinting localization technique is the most commonly used localization technique of the indoor positioning system. It is used by several technologies for short and long range position estimation like wireless fidelity and radio frequency. There are several schemes used to estimate a location for the indoor environment but the drawbacks resulted in complexity issues. These drawbacks have negative effects on location estimation. In order to address these drawbacks, this work attempted to explore the fingerprinting localization technique for location estimation of the indoor environment that focuses on position estimation. Results showed that the simplicity of the design of position estimation without compromising the functionality of the operations was observed with 100% accuracy on position estimation.</span>

scholarly journals An Indoor AC Magnetic Positioning System

2021 ◽  
Author(s):  
Paolo Carbone ◽  
Guido De Angelis ◽  
Valter Pasku ◽  
Alessio De Angelis ◽  
Marco Dionigi ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning ◽  
Experimental Results ◽  
System Level ◽  
Line Of Sight ◽  
Positioning System ◽  
Positioning Error ◽  
Geometric Configurations ◽  
Indoor Positioning System ◽  
Non Line Of Sight

<div><div><div><p>This paper describes the design and realization of a Magnetic Indoor Positioning System. The system is entirely realized using off-the-shelf components and is based on inductive coupling between resonating coils. Both system-level architecture and realization details are described along with experimental results. The realized system exhibits a maximum positioning error of less than 10 cm in an indoor environment over a 3×3 m2 area. Extensive experiments in larger areas, in non-line-of-sight conditions, and in unfavorable geometric configurations, show sub-meter accuracy, thus validating the robustness of the system with respect to other existing solutions.</p></div></div></div>

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

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 Lora RTT Ranging Characterization and Indoor Positioning System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiang Liu ◽  
XiuJun Bai ◽  
Xingli Gan ◽  
Shan Yang
Keyword(s):  
Estimation Method ◽  
Ground Truth ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Cumulative Distribution ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Systems ◽  
Ground Truth Location ◽  
Indoor Positioning System

In recent years, indoor positioning systems (IPS) are increasingly very important for a smart factory, and the Lora positioning system based on round-trip time (RTT) has been developed. This paper introduces the ranging characterization, RTT measurement, and position estimation method. In particular, a particle filter localization method-aided Lora pseudorange fitting correction is designed to solve the problem of indoor positioning; the cumulative distribution function (CDF) criteria are used to measure the quality of the estimated location in comparison to the ground truth location; when the positioning error on the x -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 61% and 99%, which are higher than the 32% and 85% without pseudorange correction. When the positioning error on the y -axis threshold is 0.2 m and 0.6 m, the CDF with pseudorange correction is 71% and 99.9%, which are higher than the 52% and 94.8% without pseudorange correction.

scholarly journals Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1496 ◽  
Author(s):  
Muhammad Ali ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Indoor Environment ◽  
Automatic Generation ◽  
Indoor Positioning ◽  
Propagation Loss ◽  
Positioning System ◽  
Site Analysis ◽  
Raster Maps ◽  
Average Accuracy ◽  
Online Feedback ◽  
Indoor Positioning System

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.

scholarly journals Indoor Positioning System Based on Global Positioning System Signals with Down- and Up-Converters in 433 MHz ISM Band

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4338
Author(s):  
Abdulkadir Uzun ◽  
Firas Abdul Ghani ◽  
Amir Mohsen Ahmadi Najafabadi ◽  
Hüsnü Yenigün ◽  
İbrahim Tekin
Keyword(s):  
Global Positioning System ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Positioning System ◽  
Gps Receiver ◽  
Ism Band ◽  
Global Positioning ◽  
Indoor Positioning System ◽  
Non Line Of Sight

In this paper, an indoor positioning system using Global Positioning System (GPS) signals in the 433 MHz Industrial Scientific Medical (ISM) band is proposed, and an experimental demonstration of how the proposed system operates under both line-of-sight and non-line-of-sight conditions on a building floor is presented. The proposed method is based on down-converting (DC) repeaters and an up-converting (UC) receiver. The down-conversion is deployed to avoid the restrictions on the use of Global Navigation Satellite Systems (GNSS) repeaters, to achieve higher output power, and to expose the GPS signals to lower path loss. The repeaters receive outdoor GPS signals at 1575.42 MHz (L1 band), down-convert them to the 433 MHz ISM band, then amplify and retransmit them to the indoor environment. The front end up-converter is combined with an off-the-shelf GPS receiver. When GPS signals at 433 MHz are received by the up-converting receiver, it then amplifies and up-converts these signals back to the L1 frequency. Subsequently, the off-the-shelf GPS receiver calculates the pseudo-ranges. The raw data are then sent from the receiver over a 2.4 GHz Wi-Fi link to a remote computer for data processing and indoor position estimation. Each repeater also has an attenuator to adjust its amplification level so that each repeater transmits almost equal signal levels in order to prevent jamming of the off-the-shelf GPS receiver. Experimental results demonstrate that the indoor position of a receiver can be found with sub-meter accuracy under both line-of-sight and non-line-of-sight conditions. The estimated position was found to be 54 and 98 cm away from the real position, while the 50% circular error probable (CEP) of the collected samples showed a radius of 3.3 and 4 m, respectively, for line-of-sight and non-line-of-sight cases.

scholarly journals Platform and Algorithm Development for a RFID-Based Indoor Positioning System

2014 ◽  
Vol 02 (03) ◽  
pp. 279-291 ◽  
Author(s):  
Han Zou ◽  
Lihua Xie ◽  
Qing-Shan Jia ◽  
Hengtao Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Support Vector ◽  
Rfid Tags ◽  
Positioning System ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Reference Tags ◽  
Indoor Positioning System

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

Sign in / Sign up

Export Citation Format

Share Document