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Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar

Publication ,  Conference
Ali, T; Richmond, CD
Published in: Proceedings of the IEEE Radar Conference
January 1, 2024

Cognitive radar systems, driven by adaptive wave-form design, enhance target detection and tracking in complex environments. In the context of channel matrix-based cognitive radar, the asymptotic distribution of the generalized likelihood ratio test (GLRT) statistic for adaptive target detection exhibits a non-central chi-square distribution. We formulate this problem as a semidefinite programming instance and propose a waveform optimization algorithm that maximizes the non-centrality parameter and hence enhances the probability of detection of the target. The proposed algorithm also incorporates power and peak-to-average power ratio (PAPR) constraints, which are crucial for ensuring practical and efficient radar operation.

Duke Scholars

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2024
 

Citation

APA
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ICMJE
MLA
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Ali, T., & Richmond, C. D. (2024). Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar. In Proceedings of the IEEE Radar Conference. https://doi.org/10.1109/RadarConf2458775.2024.10548444
Ali, T., and C. D. Richmond. “Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar.” In Proceedings of the IEEE Radar Conference, 2024. https://doi.org/10.1109/RadarConf2458775.2024.10548444.
Ali T, Richmond CD. Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar. In: Proceedings of the IEEE Radar Conference. 2024.
Ali, T., and C. D. Richmond. “Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar.” Proceedings of the IEEE Radar Conference, 2024. Scopus, doi:10.1109/RadarConf2458775.2024.10548444.
Ali T, Richmond CD. Waveform Optimization for Channel Matrix-Based Cognitive Radar/Sonar. Proceedings of the IEEE Radar Conference. 2024.

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2024