Deep Learning Approach to Source Localization of Electromagnetic Waves in the Presence of Various Sources and Noise

In this paper, the 3D localization and signal enhancement problem of a source in a noisy environment is addressed using an antenna array to ensure symmetry in communication engineering. The use of machine-learning-dependent convolutional recurrent neural networks (CRNN) and a minimum variance distortionless response (MVDR) beamformer for the localization of the source is developed. Furthermore, to ensure the adaptability of the signal enhancement module during deployment in a new environment or in new conditions, the training of a meta-learning model is conducted. At first, during the localization, the direction of arrival (DoA) estimation in both azimuth and elevation angles is generated. This is generated in a noisy three-dimensional plane and multi-source signal. Employing the DoA estimates, the MVDR is used for the enhancement of the signal source. Verifying the proposed method in the presence of mutual coupling, the two scenarios in communication engineering were simulated using a ray-tracing tool in the form of a real-world problem towards enhancing a signal source in a noisy environment and in the presence of various sources. The results obtained demonstrate how the proposed method outperforms the machine learning and parametric methods. In addition, the trained meta-learning model is employed to demonstrate how the proposed method is adaptable to any environment and still maintains an appreciable quality performance index after retraining with few data. Finally, the results obtained are motivating enough for the practical application of the proposed method.