China is working on an ultra-fast torpedo powered by AI for submarine warfare

The Star

2 days ago

In the recent Chinese blockbuster Operation Leviathan , an American nuclear submarine uses hi-tech acoustic holograms to bamboozle Chinese torpedoes and their human operators.
Months after the film hit cinema screens, military researchers in China revealed they were working on an artificial intelligence system designed to cut through exactly this type of underwater deception.
In a peer-reviewed paper published in Chinese-language journal Command Control & Simulation in April, the team from the PLA Navy Armament Department and China State Shipbuilding Corporation said their system had unprecedented accuracy for torpedoes travelling at high speeds.
Tested against data from classified high-speed torpedo ranges, the technology achieved an average 92.2 per cent success rate in distinguishing real submarines from decoys even during tense exchanges, according to the paper.
That is a leap from the legacy systems that often miss the target.
Future submarine warfare hinges on deceiving torpedoes using illusions. Hi-tech decoys – as dramatised in Operation Leviathan – are used to replicate a vessel's acoustic signature, generate a false bubble trail to make it look like it is making an emergency turn, or deploy in coordinated swarms to project ghost targets across sonar screens.
These tactics are particularly effective against what is known as ultra-fast supercavitating torpedoes – weapons that generate cavitation, or vapour bubbles, around their hulls to reduce drag. The resulting roar drowns out genuine target echoes while distorting acoustic fingerprints, according to the Chinese researchers.
'Current target recognition methods for China's underwater high-speed vehicles prove inadequate in environments saturated with advanced countermeasures, necessitating urgent development of novel approaches for feature extraction and target identification,' said the team led by senior engineers Wu Yajun and Liu Liwen.
'Only those underwater high-speed systems equipped with long-range detection capabilities and high target recognition rates can deliver sufficient operational effectiveness,' they added.
The solution they proposed came from an unorthodox combination of physics and machine learning. Facing scarce real-world combat data, the team began by simulating decoy profiles using hydrodynamic models of bubble collapse patterns and turbulence. To do that they used raw data collected from the PLA Navy's high-speed torpedo test range.
These simulations were then added to a 'generative adversarial network' – a duelling pair of AI systems. One of them, the generator, refined decoy signatures by studying submarine physics and acoustic principles. Its opponent, the discriminator, trained to detect flaws in these forgeries using seven layers of sonic pattern analysis.
After many rounds of training, the system had created a huge collection of artificial decoy profiles.
The AI uses a specialised neural network architecture inspired by image recognition, according to the paper. Sonar signals go through a process where they are normalised for amplitude, filtered through correlation receivers to suppress noise, and finally rendered as spectral 'thumbnails' using a mathematical tool known as a Fourier transform.
These sonic snapshots then pass through convolutional layers in the neural network that are tuned to detect anomalies in frequency modulation. Pooling operations then average out distortions like bubble interference.
The team said that when confronted with the most sophisticated type of decoys, detection rates went from 61.3 per cent to more than 80 per cent.
It comes amid a global race to develop 'smart' torpedoes. Russia's VA-111 Shkval torpedo and its US counterparts under development all rely on supercavitation at present, and they struggle with target discrimination at extreme speeds.
'With continuous advancements in modern underwater acoustics, electronic technologies and artificial intelligence, today's underwater battlespace often contains multiple simultaneous threats within a single operational area – including decoys, electro-acoustic countermeasure systems, electronic jammers and diverse weapon systems,' the paper said.
In such intense underwater environments where multiple targets or decoys can appear simultaneously, these systems must be able to instantly distinguish authentic targets from false ones to avoid mission failure or a wasted trajectory and to prioritise the highest-threat targets, according to the team.
'Critically, given the autonomous nature of underwater high-speed vehicles, all decisions must be made without real-time external communication support, substantially increasing algorithmic complexity and computational demands,' the team said.
'The deep-learning recognition model proposed in this study, combined with the generative adversarial networks' small-sample identification solution, enables effective underwater target discrimination. This lays the technical groundwork for field deployment,' they added. – South China Morning Post