Please use this identifier to cite or link to this item: https://doi.org/10.18494/SAM.2019.2253
Title: Improved indoor localization based on received signal strength indicator and general regression neural network
Authors: Xu, S.
Wang, Z.
Zhang, H.
Ge, S.S. 
Keywords: Filter
General regression neural network (GRNN)
Localization
Maximum likelihood estimation (MLE)
Received signal strength indicator (RSSI)
ZigBee
Issue Date: 2019
Publisher: M Y U Scientific Publishing Division
Citation: Xu, S., Wang, Z., Zhang, H., Ge, S.S. (2019). Improved indoor localization based on received signal strength indicator and general regression neural network. Sensors and Materials 31 (6) : 2043-2060. ScholarBank@NUS Repository. https://doi.org/10.18494/SAM.2019.2253
Rights: Attribution 4.0 International
Abstract: Nowadays, indoor positioning is becoming one of the most important issues in smart cities. With the rapid progress of wireless communication and digital electronic technology, wireless sensor networks (WSNs) have been developed and are playing an important role in indoor positioning systems. The received signal strength indicator (RSSI) is adopted by most range-based localization algorithms. However, the positioning system based on the RSSI is vulnerable to environmental interference and the RSS itself is unstable. To tackle this problem, we propose an improved indoor localization based on the RSSI and general regression neural network (GRNN). In the raw data processing module, an improved average filter is proposed to make the raw data stable and reliable. Then, an improved weighted centroid localization algorithm (IWCLA) is proposed to revise the positioning result on the basis of maximum likelihood estimation (MLE). In the view of the complex and changeable indoor environment, an improved GRNN localization algorithm is proposed to achieve better applicability and higher positioning accuracy. The effectiveness of the proposed methods is verified in different cases through simulation and experiment studies. © MYU K.K.
Source Title: Sensors and Materials
URI: https://scholarbank.nus.edu.sg/handle/10635/210047
ISSN: 0914-4935
DOI: 10.18494/SAM.2019.2253
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_18494_SAM_2019_2253.pdf1.71 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons