Joint Narrowband RFI Suppression and Phase Synchronization Signal Retrieval for BiSAR via Robust Principal Component Analysis

Autor: Yuesheng Chen, Yijiang Nan, Yonghua Cai, Pingping Lu, Robert Wang

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17461-17470 (2024)

Due to the complex electromagnetic environment and the increased demand for frequency occupation, radio frequency interference (RFI) is becoming a significant issue for the bistatic synthetic aperture radar (BiSAR), especially in the synchronization

Externí odkaz: https://doaj.org/article/314f7c7e6833436e84126c3730f37c45

Sparsity estimation from compressive projections via sparse random matrices

Autor: Chiara Ravazzi, Sophie Fosson, Tiziano Bianchi, Enrico Magli

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-18 (2018)

Abstract The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the pr

Externí odkaz: https://doaj.org/article/73c45a70c2cb4757b02d0ba143b3138e

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Sparsity estimation from compressive projections via sparse random matrices

Autor: Enrico Magli, Tiziano Bianchi, Sophie M. Fosson, Chiara Ravazzi

Publikováno v: Eurasip Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing
2018 (2018): 1–18. doi:10.1186/s13634-018-0578-0
info:cnr-pdr/source/autori:Ravazzi C.; Fosson S.; Bianchi T.; Magli E./titolo:Sparsity estimation from compressive projections via sparse random matrices/doi:10.1186%2Fs13634-018-0578-0/rivista:EURASIP Journal on Advances in Signal Processing (Print)/anno:2018/pagina_da:1/pagina_a:18/intervallo_pagine:1–18/volume:2018
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-18 (2018)

The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. We consider both the noise-free and the noisy settings, and we show how to extend the proposed fr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::991d692a75576b765e7f767c5669b832
https://pubmed.ncbi.nlm.nih.gov/30956656