A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation
AbstractThe 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. View Full-Text
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Chen, X.; Wang, D.; Yin, J.; Wu, Y. A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation. Sensors 2018, 18, 1925.
Chen X, Wang D, Yin J, Wu Y. A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation. Sensors. 2018; 18(6):1925.Chicago/Turabian Style
Chen, Xin; Wang, Ding; Yin, Jiexin; Wu, Ying. 2018. "A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation." Sensors 18, no. 6: 1925.
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