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Sensors, Vol. 18, Pages 1925: A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation

Sensors, Vol. 18, Pages 1925: A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation

Sensors doi: 10.3390/s18061925

Authors: Xin Chen Ding Wang Jiexin Yin Ying Wu

The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.

Authors:   Chen, Xin ; Wang, Ding ; Yin, Jiexin ; Wu, Ying
Journal:   Sensors
Volume:   18
edition:   6
Year:   2018
Pages:   1925
DOI:   10.3390/s18061925
Publication date:   13-Jun-2018
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