Angle-Doppler processing using sparse regularization

The detection of moving objects on the ground by airborne radar is one application of space-time adaptive processing (STAP). The goal is to estimate the position and velocity of objects. This paper considers the problem as a linear inverse problem and uses ℓ 1 -norm regularization to promote sparsit...

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Hauptverfasser:	Selesnick, I W, Pillai, S U, Ke Yong Li, Himed, B
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Airborne radar Clutter GMTI Inverse problems iterated thresholding Iterative algorithms Layout Object detection radar Radar applications Radar detection Sensor arrays Signal processing algorithms signal restoration sparsity STAP
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creator	Selesnick, I W Pillai, S U Ke Yong Li Himed, B
description	The detection of moving objects on the ground by airborne radar is one application of space-time adaptive processing (STAP). The goal is to estimate the position and velocity of objects. This paper considers the problem as a linear inverse problem and uses ℓ 1 -norm regularization to promote sparsity in the solution. It is proposed that the angle-Doppler plane be explicitly segmented into the clutter ridge component and a non-clutter-ridge component. We propose that the second component be modeled as sparse - as the moving objects are assumed to be well isolated in the angle-Doppler plane.
doi_str_mv	10.1109/ICASSP.2010.5496219
format	Conference Proceeding
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subjects	Airborne radar Clutter GMTI Inverse problems iterated thresholding Iterative algorithms Layout Object detection radar Radar applications Radar detection Sensor arrays Signal processing algorithms signal restoration sparsity STAP
title	Angle-Doppler processing using sparse regularization
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