recodehive / machine-learning-repos

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Radar Return Classification from the Ionosphere #1003

Closed AditiSingh-09 closed 4 months ago

AditiSingh-09 commented 4 months ago

Is there an existing issue for this?

Feature Description

This project involves classifying radar returns from the ionosphere to distinguish between "good" and "bad" radar signals. The data was collected by a high-frequency radar system deployed in Goose Bay, Labrador, which utilizes a phased array of 16 antennas and transmits at a power level of approximately 6.4 kilowatts. The primary targets of this radar system are free electrons in the ionosphere.

Use Case

Each pulse number is represented by two continuous attributes, corresponding to the complex values returned by the autocorrelation function. With 17 pulse numbers, this results in 34 continuous attributes.

Benefits

The classification of radar returns is crucial for understanding and interpreting ionospheric conditions, which can impact communication systems and satellite operations.

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Priority

High

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github-actions[bot] commented 4 months ago

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊

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github-actions[bot] commented 4 months ago

Hello @AditiSingh-09! Your issue #1003 has been closed. Thank you for your contribution!