A novel multilayer neural network model for TOA-based localization in wireless sensor networks
Paper in proceeding, 2011

A novel multilayer neural network model, called artificial synaptic network, was designed and implemented for single sensor localization with time-of-arrival (TOA) measurements. In the TOA localization problem, the location of a source sensor is estimated based on its distance from a number of anchor sensors. The measured distance values are noisy and the estimator should be able to handle different amounts of noise. Three neural network models: the proposed artificial synaptic network, a multi-layer perceptron network, and a generalized radial basis functions network were applied to the TOA localization problem. The performance of the models was compared with one another. The efficiency of the models was calculated based on the memory cost. The study result shows that the proposed artificial synaptic network has the lowest RMS error and highest efficiency. The robustness of the artificial synaptic network was compared with that of the least square (LS) method and the weighted least square (WLS) method. The Cramer-Rao lower bound (CRLB) of TOA localization was used as a benchmark. The model's robustness in high noise is better than the WLS method and remarkably close to the CRLB.


Sayed Yousef Monir Vaghefi

RMIT University

Sayed Reza Monir Vaghefi

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

2011 International Joint Conference on Neural Network, IJCNN 2011; San Jose, CA; 31 July 2011 through 5 August 2011

3079-3084 6033628
978-145771086-5 (ISBN)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering





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