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A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models

Publication ,  Conference
Ali, T; Richmond, CD
Published in: Proceedings of the IEEE Radar Conference
January 1, 2025

In this study, we analyze the performance of Generalized Likelihood Ratio Test (GLRT) statistics in MIMO radar systems under two distinct clutter modeling approaches: one that models clutter as an output of the channel matrix and another that models it within the covariance of the data distribution. This comparison reveals key trade-offs in adaptive detection performance between covariance-based and mean-based clutter modeling. By simulating a simple numerical example, we demonstrate the performance differences and identify scenarios where each model excels, providing practical insights into the conditions favoring one approach over the other for enhanced adaptive detection.

Duke Scholars

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2025
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ali, T., & Richmond, C. D. (2025). A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models. In Proceedings of the IEEE Radar Conference. https://doi.org/10.1109/RADAR52380.2025.11031784
Ali, T., and C. D. Richmond. “A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models.” In Proceedings of the IEEE Radar Conference, 2025. https://doi.org/10.1109/RADAR52380.2025.11031784.
Ali T, Richmond CD. A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models. In: Proceedings of the IEEE Radar Conference. 2025.
Ali, T., and C. D. Richmond. “A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models.” Proceedings of the IEEE Radar Conference, 2025. Scopus, doi:10.1109/RADAR52380.2025.11031784.
Ali T, Richmond CD. A Comparison of Channel Matrix-Based and Covariance-Based Clutter Models. Proceedings of the IEEE Radar Conference. 2025.

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2025