Learning modulation filter networks for weak signal detection in noise

Highlights：

• The contributions of our work include:

• New modulation filters that are employed to refine signal filters, leading to a new architecture for CNNs model. The convolution operation is further improved by the learned filters. LMFNs are highly compressed, yet achieving state-of-the-art performance.

• Solving LMFNs in an end-to-end framework with a two-stage optimization scheme. These LMFNs outperform all the state-of-the-art models, and can solve the weak signal detection problem under strong and complex background noise with unknown covariance.

• Establishing a weak signal dataset that contains UAV communication signals in a real-terrain environment. The dataset is rich in attributes and useful for training networks like LMFNs.