UPDATE: Researchers have just announced a groundbreaking method for recognizing underwater communication signals that mimic dolphin whistles. This innovative technology, developed by a team from Tianjin University and the Shenyang Institute of Automation, promises to revolutionize maritime communication, particularly in military applications.
The recognition method tackles the pressing issue of underwater bionic camouflage covert communication (UBCCC) signals, which have historically posed challenges for traditional recognition systems. Conventional techniques often misclassify these signals as natural sounds, creating significant vulnerabilities in underwater operations. The new approach employs a convolutional neural network (CNN) to achieve an impressive 90% recognition accuracy under challenging conditions, including a signal-to-noise ratio (SNR) of 0 dB during simulations.
The team’s method is particularly crucial for military confrontations, where reliable communication is vital. Traditional signals, constructed artificially, do not effectively blend into the natural underwater noise, making them easier to detect. In contrast, UBCCC signals, which closely mimic marine mammal calls, offer higher concealment and deception, posing a serious risk to operational security.
This urgent breakthrough utilizes a three-step process to enhance underwater signal recognition:
1. **Spatial Diversity Combining (SDC)**: This initial step mitigates signal fading caused by multipath propagation in underwater environments. By combining signals from multiple hydrophones, SDC strengthens the primary signal while suppressing interference from other sources.
2. **Time-Frequency Spectrum (TFS) Mask Filtering**: Using advanced filtering techniques, researchers convert the TFS into a 2D image to extract whistle signals from noisy backgrounds. This includes applying mean filtering and median filtering to isolate time-frequency contours and develop a TFS mask for precise extraction.
3. **Phase Derivative Spectrum and CNN Recognition**: The final step involves applying the Hilbert transform to the extracted signals, allowing for the calculation of phase derivatives. By generating a phase derivative scalogram image, the CNN can effectively distinguish between CVCFMS signals and traditional dolphin whistles, thanks to unique mutation points that signify symbol connections.
Extensive validations, including lake experiments at Tianjin University’s Qingnian Lake, confirm the method’s effectiveness. In these tests, conducted over a distance of 150 m with an SNR of 6.36 dB, researchers achieved a recognition accuracy of 81%. While slightly lower than simulations, these results underscore the method’s capability in real-world conditions.
The implications of this research are significant for both military and civilian applications, enhancing communication security and operational effectiveness in underwater environments. The study, titled “Recognition method for underwater communication signals that mimic dolphin whistles using phase-shifting modulation,” is authored by Qingwang YAO, Jiajia JIANG, Xiaolong YU, Zhuochen LI, Xiaozong HOU, Xiao FU, and Fajie DUAN.
For more details, the full open-access paper is available at: https://doi.org/10.1631/FITEE.2400572.
As this technology continues to develop, experts and military officials will be monitoring its integration into current communication systems, marking a pivotal shift in how underwater interactions are managed. Stay tuned for further updates on this urgent and exciting advancement in underwater communication technology.
