In-building localization using neural networks

被引：0
|
作者：
Ahmad, Uzair [1 ]
Gavrilov, Andrey [1 ]
Nasir, Uzma [2 ]
Iqbal, Mahrin [2 ]
Cho, Seong Jin [1 ]
Lee, Sungyoung [1 ]
机构：
[1] Kyung Hee Univ, Dept Comp Engn, Seoul, South Korea
[2] NUST Inst IT, Rawalpindi, Pakistan
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON ENGINEERING OF INTELLIGENT SYSTEMS | 2006年
关键词：
artificial neural networks; location aware computing; wireless LAN;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Location Awareness is key capability of Context-Aware Ubiquitous environments. Received Signal Strength (RSS) based localization is increasingly popular choice especially for indoor scenarios after pervasive adoption of IEEE 802.11 Wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multi-path propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ Modular Multi-Layer Perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.
引用
收藏
页码：36 / +
页数：2
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