Application of a Neural Network Based on Physical Information in Weak Signal Detection

With the development of science and technology and the progress of measurement means, weak signal detection is playing an increasingly important role in scientific research and engineering applications. However, traditional detection methods often struggle to achieve ideal detection results in low signal to noise ratio and complex noise environment. This paper proposes a neural network method combining physical information constraints, which improves the accuracy and reliability of weak signal detection by integrating the physical properties of signals into the network design. The experimental results show that this method can significantly improve the detection performance while maintaining a low computational complexity, and provides a new technical path for weak signal detection.