Bharat in Betastartup field guide
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No. 1288Problem statementDefence and aerospace

Smart Scan strategy for Electronic Warfare

Source

Source summary

The challenge concerns machine-learning scheduling for electronic-support receivers when prior information about emitters is unreliable. The requested output is receiver-scheduler software evaluated in simulation. This is a high-level account of a defence research topic; interception strategies, detailed operational scheduling and performance-optimization instructions are intentionally not reproduced.

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.