Title :
Intelligent CFAR processor based on data variability
Author :
Smith, Michael E. ; Varshney, Pramod K.
Author_Institution :
Sensis Corp., DeWitt, NY, USA
fDate :
7/1/2000 12:00:00 AM
Abstract :
An intelligent constant false alarm rate (CFAR) processor to perform adaptive threshold target detection is presented. It employs a composite approach based on the well-known cell averaging CFAR (CA-CFAR), smallest of CFAR (SO-CFAR), and greatest of CFAR (GO-CFAR) processors. Data in the reference window is used to compute a second-order statistic called the variability index (VI) and the ratio of the means of the leading and lagging windows. Based on these statistics, the VI-CFAR dynamically tailors the background estimation algorithm. The VI-CFAR processor provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments including multiple targets and extended clutter edges
Keywords :
adaptive signal detection; estimation theory; probability; radar detection; statistical analysis; target tracking; adaptive threshold target detection; cell averaging CFAR; data variability; extended clutter edges; homogeneous environment; intelligent CFAR processor; intelligent constant false alarm rate processor; lagging windows; leading windows; low loss CFAR performance; multiple targets; nonhomogeneous environments; reference window; second-order statistic; variability index; Background noise; Clutter; Computer science; Object detection; Performance loss; Radar detection; Robustness; Statistics; Testing; Working environment noise;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
