RECOVERY LIMITATIONS OF MEG SOURCE LOCALIZATION MODEL FOR EPILEPSY

In this contribution, we present a high-resolution and accurate sound source localization via a deep learning framework. While the spherical microphone arrays can be utilized to produce omnidirectional beams, it is widely known that the conventional spherical harmonics beamforming (SHB) has a limit in terms of its spatial resolution. To accomplish the sound source localization with high resolution and preciseness, we propose a convolutional neural network (CNN)-based source localization model as a way of a data-driven approach. We first present a novel way to define the source distribution map that can spatially represent the single point source's position and strength. By utilizing paired dataset with spherical harmonics beamforming maps and our proposed high-resolution maps, we develop a fully convolutional neural network based on the encoder-decoder structure for establishing the image-to-image transformation model. Both quantitative and qualitative results are demonstrated to evaluate the powerfulness of the proposed data-driven source localization model.

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Simulation studies of multiple dipole neuromagnetic source localization: model order and limits of source resolution

IEEE Transactions on Biomedical Engineering ◽

10.1109/10.237672 ◽

1993 ◽

Vol 40 (6) ◽

pp. 529-540 ◽

Cited By ~ 115

Author(s):

S. Supek ◽

C.J. Aine

Keyword(s):

Source Localization ◽

Simulation Studies ◽

Model Order ◽

Localization Model ◽

Source Localization Model

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Passive Radar Source Localization Based on Cuckoo Search NVTDOA

International Journal of Antennas and Propagation ◽

10.1155/2020/1410692 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Gang Niu ◽

Jie Gao ◽

Tai-hang Du

Keyword(s):

Source Localization ◽

Search Algorithm ◽

Sufficient Conditions ◽

Pulse Signal ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Passive Radar ◽

Electronic Warfare ◽

Periodic Rotation ◽

Localization Model

In order to meet the requirement of passive radar source localization in electronic warfare, the concept of the virtual time differences of arrival (VTDOA) is proposed by taking advantage of the characteristics of the same UAV in different positions at different times and the periodic rotation of radar pulse signal. The VTDOAs are the passive localization information defined as the time differences of the radar pulse transmission from the radar position to different virtual receivers. Firstly, a nonlinear VTDOA (NVTDOA) localization model is constructed. Moreover, sufficient conditions for accurately calculating the periodic integers in the model are analyzed, and the observability conditions of the localization model determined are deduced. Secondly, the convergence solution of the NVTDOA localization equation is obtained by Cuckoo search algorithm; thus, passive radar source localization is realized. Finally, the performance of the proposed method is verified by comparing with the existing methods.

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