AI Submarine Detection & Communication
Budget: ₹75,000 – ₹150,000 INR
I’m developing an end-to-end, AI-driven solution that can both detect submarines and forward the resulting data to command assets. The system must work just as reliably on the continental shelf as it does in blue-water depths, so models have to be trained and tested for very different acoustic profiles, temperature layers, and background traffic.
What I need from you
• A detection engine that ingests raw sonar or hydrophone streams, removes noise, and classifies contacts in real time. Techniques you’re comfortable with—whether CNNs, transformers, or classical signal processing—are welcome as long as you can justify accuracy and latency numbers.
• A communication relay module able to switch seamlessly between underwater acoustic links and an RF uplink once the node reaches the surface or a tethered buoy. Protocol choice is open, but interoperability with standard naval radios is essential.
• Clear documentation and a reproducible test environment (MATLAB, Python/TensorFlow, PyTorch, or an equivalent toolchain) so I can run benchmarks in both deep-sea and coastal-water datasets.
• A short demo video or live session proving that simulated contacts are detected and forwarded without packet loss beyond the agreed threshold.
Acceptance criteria
1. ≥ 90 % correct classification of submarine vs. non-submarine targets on the supplied mixed-environment dataset.
2. End-to-end detection-to-relay latency under 2 s for a 5 km acoustic hop plus RF uplink.
3. Full hand-off logging between acoustic and RF channels with no dropped messages.
If you have prior work on maritime AI, sonar, or hybrid communications, point me to it; otherwise, outline how you’d tackle training data, channel modelling, and integration testing. I’m ready to start as soon as we agree on milestones and an evaluation schedule.
What I need from you
• A detection engine that ingests raw sonar or hydrophone streams, removes noise, and classifies contacts in real time. Techniques you’re comfortable with—whether CNNs, transformers, or classical signal processing—are welcome as long as you can justify accuracy and latency numbers.
• A communication relay module able to switch seamlessly between underwater acoustic links and an RF uplink once the node reaches the surface or a tethered buoy. Protocol choice is open, but interoperability with standard naval radios is essential.
• Clear documentation and a reproducible test environment (MATLAB, Python/TensorFlow, PyTorch, or an equivalent toolchain) so I can run benchmarks in both deep-sea and coastal-water datasets.
• A short demo video or live session proving that simulated contacts are detected and forwarded without packet loss beyond the agreed threshold.
Acceptance criteria
1. ≥ 90 % correct classification of submarine vs. non-submarine targets on the supplied mixed-environment dataset.
2. End-to-end detection-to-relay latency under 2 s for a 5 km acoustic hop plus RF uplink.
3. Full hand-off logging between acoustic and RF channels with no dropped messages.
If you have prior work on maritime AI, sonar, or hybrid communications, point me to it; otherwise, outline how you’d tackle training data, channel modelling, and integration testing. I’m ready to start as soon as we agree on milestones and an evaluation schedule.