OS Characterization for Local CFAR Detection

Kevin D. Donohue

doi:10.1109/21.120072

OS Characterization for Local CFAR Detection

Kevin D. Donohue

Electrical and Computer Engineering

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

4 Scopus citations

Abstract

An order statistic (OS) characterization for modeling clutter statistics at local detectors is presented. Important features of the OS characterization include its structure, allowing for parallel computations, and its degrees of freedom, which enable it to track various changes in the clutter statistics. Constant false alarm rate (CFAR) performance for local detectors designed from the OS characterization is compared to conventional CFAR detectors that operate locally at each receiver. The results demonstrate the ability of the local OS detectors to adapt to changes in the skewness of the clutter distribution. For the cases tested, the local OS detectors maintain the expected value for the false alarm probability better than the local conventional CFAR detectors by factors ranging from 2 to 100. OS detectors also provide information to the data fusion center that can be useful in discriminating different types of interference and detecting small targets in clutter. Examples of utilizing OS detections at the data fusion center are discussed.

Original language	English
Pages (from-to)	1212-1216
Number of pages	5
Journal	IEEE Transactions on Systems, Man and Cybernetics
Volume	21
Issue number	5
DOIs	https://doi.org/10.1109/21.120072
State	Published - 1991

Bibliographical note

Funding Information:
Manuscript received August 24, 1990; revised March 1, 1991. This work was supported by the SDIO/IST managed by the Office of Naval Research under Contract N000014-86-K-0520. K.D. Donohue was with the Drexel University, Philadelphia, PA. He is now with the Department of Electrical Engineering, University of Kentucky, 453 Anderson Hall, Lexington, KY 40506-0046. N.M. Bilgutay is with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104. IEEE Log Number 9102029.

Funding

Manuscript received August 24, 1990; revised March 1, 1991. This work was supported by the SDIO/IST managed by the Office of Naval Research under Contract N000014-86-K-0520. K.D. Donohue was with the Drexel University, Philadelphia, PA. He is now with the Department of Electrical Engineering, University of Kentucky, 453 Anderson Hall, Lexington, KY 40506-0046. N.M. Bilgutay is with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104. IEEE Log Number 9102029.

Funders	Funder number
Office of Naval Research	N000014-86-K-0520

ASJC Scopus subject areas

General Engineering

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10.1109/21.120072

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title = "OS Characterization for Local CFAR Detection",

abstract = "An order statistic (OS) characterization for modeling clutter statistics at local detectors is presented. Important features of the OS characterization include its structure, allowing for parallel computations, and its degrees of freedom, which enable it to track various changes in the clutter statistics. Constant false alarm rate (CFAR) performance for local detectors designed from the OS characterization is compared to conventional CFAR detectors that operate locally at each receiver. The results demonstrate the ability of the local OS detectors to adapt to changes in the skewness of the clutter distribution. For the cases tested, the local OS detectors maintain the expected value for the false alarm probability better than the local conventional CFAR detectors by factors ranging from 2 to 100. OS detectors also provide information to the data fusion center that can be useful in discriminating different types of interference and detecting small targets in clutter. Examples of utilizing OS detections at the data fusion center are discussed.",

author = "Donohue, {Kevin D.}",

note = "Funding Information: Manuscript received August 24, 1990; revised March 1, 1991. This work was supported by the SDIO/IST managed by the Office of Naval Research under Contract N000014-86-K-0520. K.D. Donohue was with the Drexel University, Philadelphia, PA. He is now with the Department of Electrical Engineering, University of Kentucky, 453 Anderson Hall, Lexington, KY 40506-0046. N.M. Bilgutay is with the Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, 19104. IEEE Log Number 9102029.",

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PY - 1991

Y1 - 1991

N2 - An order statistic (OS) characterization for modeling clutter statistics at local detectors is presented. Important features of the OS characterization include its structure, allowing for parallel computations, and its degrees of freedom, which enable it to track various changes in the clutter statistics. Constant false alarm rate (CFAR) performance for local detectors designed from the OS characterization is compared to conventional CFAR detectors that operate locally at each receiver. The results demonstrate the ability of the local OS detectors to adapt to changes in the skewness of the clutter distribution. For the cases tested, the local OS detectors maintain the expected value for the false alarm probability better than the local conventional CFAR detectors by factors ranging from 2 to 100. OS detectors also provide information to the data fusion center that can be useful in discriminating different types of interference and detecting small targets in clutter. Examples of utilizing OS detections at the data fusion center are discussed.

AB - An order statistic (OS) characterization for modeling clutter statistics at local detectors is presented. Important features of the OS characterization include its structure, allowing for parallel computations, and its degrees of freedom, which enable it to track various changes in the clutter statistics. Constant false alarm rate (CFAR) performance for local detectors designed from the OS characterization is compared to conventional CFAR detectors that operate locally at each receiver. The results demonstrate the ability of the local OS detectors to adapt to changes in the skewness of the clutter distribution. For the cases tested, the local OS detectors maintain the expected value for the false alarm probability better than the local conventional CFAR detectors by factors ranging from 2 to 100. OS detectors also provide information to the data fusion center that can be useful in discriminating different types of interference and detecting small targets in clutter. Examples of utilizing OS detections at the data fusion center are discussed.

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OS Characterization for Local CFAR Detection

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