Source Localization of EM Wave in the Presence of neighboring Sources and Noisy Environments using Deep Learning and Meta Learning

The 3D localization and signal enhancement problem of a source in a noisy environment is addressed using antenna array. The use of machine learning dependent convolutional recurrent neural networks (CRNN) and minimum variance distortionless response (MVDR) beamformer for the localization of source is developed. Furthermore, ensuring the adaptability of the signal enhancement module during deployment in new environment or in new condition, the training of a meta learning model is conducted. Verifying the proposed method in the presence of mutual coupling, the two scenarios in communication engineering were simulated using ray tracing tool, in form of real world problem towards enhancing a signal source in a noisy environment and in the presence of various sources. In addition, the trained meta learning model is employed to demonstrate how the proposed method is adaptable to any environments, and still maintains appreciable quality performance index after retraining with few data.