Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations

—A new and robust constant false alarm rate (CFAR) detector based on truncated statistics is proposed for ship detection in single-look intensity (SLI) and multi-look intensity (MLI) synthetic aperture radar (SAR) data. The approach is aimed at high target density situations, such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers, and truncated statistics to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high target density situations.

• Type: Journal
• Year: 2016
• Where: TGRS
• Authors: Ding Tao, Stian Normann Anfinsen, Camilla Brekke
• Keywords: TGRS 2016