Bharat in Betastartup field guide
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No. 385Problem statementDefence and aerospace

Enhancing underwater domain awareness (UDA) by the use of artificial intelligence/machine learning or other novel techniques

Source

Source summary

Abridged source description: fuse large volumes of underwater sensor information, including sonar and sonobuoy observations, to distinguish contacts in noisy conditions. AI/ML is proposed but explicitly not mandatory; improved signal processing or decision support may also satisfy the request. Military submarine-detection implementation is omitted.

No. 173Problem statementDefence and aerospace

AI-ML Modelling - AI-ML models for space applications

Develop AI/ML models for large space datasets. The source discusses Earth-observation and spacecraft telemetry analysis, as well as support for large satellite constellations, including relative positioning, communications and end-of-life management.

No. 360Problem statementDefence and aerospace

Data analytics for air trajectory

Develop a machine-learning algorithm using historical aircraft trajectories to identify anomalous movements and departures from authorised routes. The source provides that mechanism and data basis but no further operational or performance specification.

No. 366Problem statementDefence and aerospace

Predictive preventive and prescriptive machine monitoring

Develop a modular platform monitoring ship machinery in real time through parameters such as temperature, pressure and sound. AI should support predictive and preventive maintenance, while the system also gives ship staff proposed corrective actions when faults arise. The prescriptive component is part of the original request, not later commentary.