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Probabilistic Neural Network for RSS-Based Collaborative Localization

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4 Author(s)
Peisen Zhao ; Dept. of Electron. Eng., Tsinghua Univ., Beijing, China ; Chunxiao Jiang ; Chen, H. ; Yong Ren

One critical challenge for accurate localization with Received Signal Strength Indicator (RSSI) is the anisotropic environment, which causes the RSS-Distance Relationship (RDR) to vary spatially. To alleviate localization error caused by RDR anisotropy, most of existing works adopt multiple RDR algorithms. However, we have found that the arbitrary RDR selection in these algorithms can lead to large localization error. Moreover, localization accuracy can be further enhanced by utilizing information provided by more Access Points (APs). To address these problems, we propose a Probabilistic Neural Network based localization algorithm in this paper. The algorithm features two steps: Global Optimization and Regional Compensation, during which all APs exchange information about the Blind Node (BN) to locate it collaboratively. Simulation result shows that the proposed algorithm can achieve a localization accuracy 35% higher than that of multiple RDR algorithms.

Published in:

Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th

Date of Conference:

6-9 May 2012