Sea Clutter Suppression Based on Complex-Valued Neural Networks Optimized by PSD
- Resource Type
- article
- Authors
- Hongling Zhu; Ze Yu; Jindong Yu
- Source
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 9821-9828 (2022)
- Subject
- Complex-valued neural networks
power spectral density (PSD)
sea clutter suppression
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
- Language
- English
- ISSN
- 2151-1535
Sea clutter suppression plays an important role in improving the estimation accuracy of the motion parameters of moving ships. Based on the chaotic characteristics of sea clutter, a novel sea clutter suppression method based on complex-valued neural networks optimized by power spectral density is proposed. The complex-valued neural networks helped reduce the phase prediction error of sea clutter, so that the sea clutter prediction accuracy was significantly improved compared with that of real-valued neural networks. The power spectral density function was added to the loss function of the complex-valued neural networks to optimize the training of the networks, and the prediction accuracy was further improved. The sea clutter could be effectively suppressed by cancellation and the signal-to-clutter ratio of the echo was improved. Experimental results based on measured sea clutter data validated the proposed method.