Skew log-normal channel model for indoor cooperative localization

The performance of cooperative localization using received signal strength (RSS) benefits from accurate radio channel modeling. While log-normal shadowing is commonly used to model the relationship between RSS and range, the RSS error distribution in indoor environments has been observed to be neith...

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Bibliographic Details
Main Authors: Sung, Chang Kyung, Li, Shenghong, Hedley, Mark, Nikolic, Nasiha, Ni, Wei
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric modeling
Channel models
Computational modeling
Data models
Peer-to-peer computing
Shadow mapping
Online Access:Request full text
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Summary:The performance of cooperative localization using received signal strength (RSS) benefits from accurate radio channel modeling. While log-normal shadowing is commonly used to model the relationship between RSS and range, the RSS error distribution in indoor environments has been observed to be neither normal nor symmetric. In this paper, we propose a skew log-normal channel model, which includes the standard log-normal model as a special case. We further propose an algorithm for using this model for RSS based cooperative localization. The algorithm was evaluated using data from an electro-magnetic simulation of an aircraft cabin, and was shown to generate more accurate node locations compared to the use of log-normal shadowing in the same localization algorithm.
ISSN:2166-9589
DOI:10.1109/PIMRC.2017.8292294