.png){kind=icon}
.png){kind=icon}
Return
Computationally efficient CFAR detector for K-distribution SAR clutter
.png){kind=icon}
PRE
AI
DOI：10.1080/01431161.2025.2549535.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a target detection algorithm forK-distributed Synthetic Aperture Radar (SAR) clutter data. We combine texture estimation and constant false alarm rate (CFAR) detection through a maximum a posteriori (MAP) estimator for the texture in intensity format. The CFAR detector determines the threshold forΓ-distributed background clutter texture. We provide a closed-form expression for the CFAR detection threshold. Furthermore, a closed-form expression for the detection probability has been derived for the proposed CFAR detector. Analytical results are presented to assess the CFAR detection performance. We further assess the effectiveness of the CFAR detector on Moving and Stationary Target Acquisition and Recognition (MSTAR) data. Experimental results illustrate the computational effectiveness of the proposed detector over conventional CFAR-Kdetectors while attaining improved detection accuracy compared to the CFAR-WBL, CFAR-LGN, and existing CFAR-Kdetectors.
Keywords:
Constant false alarm rate (CFAR)
K-distribution
maximum-a-posteriori (MAP) estimation
log-cumulants method
Journal
IF:
2.6
Papers:
1.2W
Citations:
2.7W