Design of CFAR radars using compressive sensing

Haley H. Kim; Alexander M. Haimovich

doi:10.1109/RADAR.2016.7485280

Design of CFAR radars using compressive sensing

Haley H. Kim, Alexander M. Haimovich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

In this work we propose the GLRT-MP algorithm which combines compressed sensing techniques and classical detection theory and explores its application to sparse arrays. Sparse arrays are large undersample arrays with nonuniform spacing that provides high resolution at the cost of high sidelobes. Compressed sensing techniques are able to minimize the undesired effects of the large array, while classical detection theory provides a way to perform detection while maintaining a desired false alarm probability. We provide analysis of the GLRT when the noise power is known and unknown, the latter which will allow one to design a CFAR radar. We provide numerical results to verify our results.

Original language	English (US)
Title of host publication	2016 IEEE Radar Conference, RadarConf 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509008636
DOIs	https://doi.org/10.1109/RADAR.2016.7485280
State	Published - Jun 3 2016
Event	2016 IEEE Radar Conference, RadarConf 2016 - Philadelphia, United States Duration: May 2 2016 → May 6 2016

Other

Other	2016 IEEE Radar Conference, RadarConf 2016
Country/Territory	United States
City	Philadelphia
Period	5/2/16 → 5/6/16

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications
Instrumentation

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10.1109/RADAR.2016.7485280

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title = "Design of CFAR radars using compressive sensing",

abstract = "In this work we propose the GLRT-MP algorithm which combines compressed sensing techniques and classical detection theory and explores its application to sparse arrays. Sparse arrays are large undersample arrays with nonuniform spacing that provides high resolution at the cost of high sidelobes. Compressed sensing techniques are able to minimize the undesired effects of the large array, while classical detection theory provides a way to perform detection while maintaining a desired false alarm probability. We provide analysis of the GLRT when the noise power is known and unknown, the latter which will allow one to design a CFAR radar. We provide numerical results to verify our results.",

author = "Kim, {Haley H.} and Haimovich, {Alexander M.}",

year = "2016",

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doi = "10.1109/RADAR.2016.7485280",

language = "English (US)",

booktitle = "2016 IEEE Radar Conference, RadarConf 2016",

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note = "2016 IEEE Radar Conference, RadarConf 2016 ; Conference date: 02-05-2016 Through 06-05-2016",

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AU - Haimovich, Alexander M.

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N2 - In this work we propose the GLRT-MP algorithm which combines compressed sensing techniques and classical detection theory and explores its application to sparse arrays. Sparse arrays are large undersample arrays with nonuniform spacing that provides high resolution at the cost of high sidelobes. Compressed sensing techniques are able to minimize the undesired effects of the large array, while classical detection theory provides a way to perform detection while maintaining a desired false alarm probability. We provide analysis of the GLRT when the noise power is known and unknown, the latter which will allow one to design a CFAR radar. We provide numerical results to verify our results.

AB - In this work we propose the GLRT-MP algorithm which combines compressed sensing techniques and classical detection theory and explores its application to sparse arrays. Sparse arrays are large undersample arrays with nonuniform spacing that provides high resolution at the cost of high sidelobes. Compressed sensing techniques are able to minimize the undesired effects of the large array, while classical detection theory provides a way to perform detection while maintaining a desired false alarm probability. We provide analysis of the GLRT when the noise power is known and unknown, the latter which will allow one to design a CFAR radar. We provide numerical results to verify our results.

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DO - 10.1109/RADAR.2016.7485280

M3 - Conference contribution

AN - SCOPUS:84978271690

BT - 2016 IEEE Radar Conference, RadarConf 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Radar Conference, RadarConf 2016

Y2 - 2 May 2016 through 6 May 2016

ER -

Design of CFAR radars using compressive sensing

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