Localization of RF Emitters using Convolutional Neural Networks under Sparse Prior

With the application of integrated sensing and communication, radiated source localization has gradually become a popular research direction. Radiation source localization has more applications in reality, for example, in earthquake disaster scenarios, entrapped individuals can be found by using terminal devices. The traditional methods suffer from degradation of performance under low signal-to-noise ratio (SNR) conditions and cannot effectively deal with complex propagation environments. A signal direction of arrival (DOA) localization method based on convolutional neural networks is proposed to achieve high resolution localization of single or multiple radio frequency (RF) radiation sources in scenarios with low SNR and adjacent sources. The experiment shows that the proposed method has good performance in single target and multi-target localization. In addition, the proposed method still has good estimation performance in environments with small signal source angle intervals and varying SNR.