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In this study, authors address high-range-resolution (HRR) profile reconstruction, when stepped-frequency waveforms are eventually used to maintain a narrow instantaneous bandwidth, resorting to the sparse learning via iterative minimisation (SLIM) paradigm, a regularised minimisation approach with an lqlq -norm constraint (for 0

HRR profile estimation using SLIM (High Range Resolution Profile Estimation using Sparse Learning via Iterative Minimization)

Pia Addabbo
;Antonio De Maio
;Silvia Liberata Ullo
2019-01-01

Abstract

In this study, authors address high-range-resolution (HRR) profile reconstruction, when stepped-frequency waveforms are eventually used to maintain a narrow instantaneous bandwidth, resorting to the sparse learning via iterative minimisation (SLIM) paradigm, a regularised minimisation approach with an lqlq -norm constraint (for 0
2019
learning (artificial intelligence) , Bayes methods , minimisation , least squares approximations , iterative methods , maximum likelihood estimation , radar resolution
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/38505
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