Intelligent CFAR processor based on data variability

Michael E. Smith; Pramod K. Varshney

doi:10.1109/7.869503

Intelligent CFAR processor based on data variability

Michael E. Smith, Pramod K. Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

238 Scopus citations

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.

Original language	English (US)
Pages (from-to)	837-847
Number of pages	11
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	36
Issue number	3 PART 1
DOIs	https://doi.org/10.1109/7.869503
State	Published - 2000

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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title = "Intelligent CFAR processor based on data variability",

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.",

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AB - 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.

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Intelligent CFAR processor based on data variability

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