Anti-Jamming Method of Near-Field Underwater Acoustic Detection Based on WGAN
- Resource Type
- Conference
- Authors
- Jingbo, Zhang; Zhe, Jiang; Daojiang, Li; Haiyan, Wang
- Source
- 2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Signal Processing, Communications and Computing (ICSPCC), 2023 IEEE International Conference on. :1-5 Nov, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Training
Speech enhancement
Generative adversarial networks
Stability analysis
Generators
Jamming
Underwater acoustics
underwater object detection
anti-jamming
generative adversarial network
- Language
- ISSN
- 2837-116X
This paper analyzes the issue of artificial interference encountered in underwater near-field detection. We briefly examines three common types of artificial jamming signal and their mechanisms. Taking inspiration from the application of Generative Adversarial Networks in speech signal enhancement, this study employs Wasserstein GAN and integrates the characteristics of detection signals. L2 loss is added to the generator's loss function in WGAN to enhance training stability. Simulation analysis demonstrates that the trained WGAN generator effectively combats the three types of artificial jamming.