Jamming Signal Power Detection Based On Masked Convolutional Encoder-Decoder Network

Jamming signal power detection (JPD) is a crucial step in wireless jamming cognition technology. By detecting the power values of multiple jamming signals, prior information can be provided for the dynamic optimization of anti-jamming strategies, thereby effectively improving the communication system’s resilience to jamming signals. However, in complex electromagnetic countermeasures environments, the radar receivers frequently encounter a multitude of composite jamming signals emitted by multiple jammers, which exhibit significant overlap across temporal and spectral domains. Furthermore, the diversity and uncertainty of multiple jamming signals result in an explosive growth of composite labels, posing challenges to existing deep learning algorithms. Hence, our study presents JPDNet, an innovative framework for detecting multiple jamming signals’ power. The model consists of a deep convolutional encoder/decoder paired with a masks extractor, enabling the reconstruction and power detection of multiple jamming signals. This JPD approach can effectively overcome the limitation of composite labels. The stacked multi-scale temporal convolutional networks (M-TCN) function as the backbone of the masks extractor, aiming to capture the long-term dependencies, and fully extract multi-scale features from the input jamming signals. Great experimental results indicate that, contrasted to the most relevant approaches, JPDNet exhibits superior power detection performance.