We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery.
- Authors
Jiaqiu Ai; Xuezhi Yang; Fang Zhou; Zhangyu Dong; Lu Jia; He Yan
- Abstract
Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.
- Subjects
CONSTANT false alarm rate (Data processing); RADAR equipment on ships; CLUTTER (Radar); TWO-dimensional materials (Nanotechnology); PARAMETER estimation
- Publication
Sensors (14248220), 2017, Vol 17, Issue 4, p686
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s17040686