Research output per year
Research output per year
Project Details
| Acronym | Sub-THz Radar Sensing |
|---|---|
| Status | Finished |
| Effective start/end date | 1/01/20 → 30/06/23 |
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Machine learning-based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures
Rahman, S., Vattulainen, A. B. & Robertson, D. A., Feb 2024, In: IET Radar Sonar and Navigation. 18, 2, p. 344-360 17 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Radar signatures of sea lions at K-band and W-band
Rahman, S., Vattulainen, A. B., Robertson, D. A. & Milne, R., 29 Jan 2024, In: IET Radar Sonar and Navigation. 18, 1, p. 147-157 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Amplitude distribution of low grazing angle G-band littoral sea clutter
Vattulainen, A. B., Rahman, S., Stove, A. G. & Robertson, D. A., 28 Dec 2023, 2023 IEEE International Radar Conference (RADAR). New York, NY: IEEE, 6 p. 10371097Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile
Datasets
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Amplitude Characteristics of Littoral Sea Clutter Data at K-band and W-band (dataset)
Rahman, S. (Creator), Vattulainen, A. B. (Contributor) & Robertson, D. (Contributor), University of St Andrews, 27 May 2022
DOI: 10.17630/85286f40-a781-4c34-aaa3-abd6fcfb965f
Dataset
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Millimetre wave radar signatures of sea lions (dataset)
Rahman, S. (Creator), Vattulainen, A. B. (Contributor), Robertson, D. (Supervisor) & Milne, R. (Contributor), University of St Andrews, 15 May 2023
DOI: 10.17630/bbcde7e9-a2e1-4a34-8df9-f7d07937f05e
Dataset
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Machine learning based approach for maritime target classification and anomaly detection using millimetre wave radar Doppler signatures (dataset)
Rahman, S. (Creator), Vattulainen, A. B. (Contributor) & Robertson, D. (Supervisor), University of St Andrews, 8 Dec 2023
DOI: 10.17630/2bb28a35-11a7-4d86-8464-75288e6b66ce
Dataset
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