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One Recurrent Neural Networks Solution for Passive Localization

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Abstract

A revised recurrent neural networks method for the passive source localization is proposed in this paper. The cost function has been designed which makes the transformation from transmitters’ localization into the outputs of the settled revised recurrent neural networks through spatial partition. The neural networks are chaotic and stable in convergence. The received signal model is constructed firstly. The parameters of the recurrent neural networks have been trained properly according to the scene. The experiments and analysis display that the revised recurrent neural networks solution not only obtains high precision location, but also has high convergence rate.

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Authors and Affiliations

  1. Electronic Engineering Department, Zhengzhou Institute of Information Science and Technology, No. 62, Science Road, High-Tech District, P. O. Box: 450000, Zhengzhou City, 450001, Henan Province, People’s Republic of China

    Chuang Zhao & Yongjun Zhao

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  1. Chuang Zhao
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  2. Yongjun Zhao
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Correspondence to Chuang Zhao.

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Zhao, C., Zhao, Y. One Recurrent Neural Networks Solution for Passive Localization. Neural Process Lett 49, 787–796 (2019). https://doi.org/10.1007/s11063-018-9856-y

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