Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging

In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered sim...

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Main Authors: P. Huang, X. Li, H. Wang
Format: Article
Language:English
Published: Spolecnost pro radioelektronicke inzenyrstvi 2018-06-01
Series:Radioengineering
Subjects:
Online Access:https://www.radioeng.cz/fulltexts/2018/18_02_0580_0586.pdf
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author P. Huang
X. Li
H. Wang
author_facet P. Huang
X. Li
H. Wang
author_sort P. Huang
collection DOAJ
description In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered simultaneously and a new compressive sensing imaging algorithm named tensor-based match pursuit(TMP) is proposed. In the proposed method, MIMO radar tensor signal model is established to eliminate “dimension disaster” at first. Then, exploiting tensor decomposition to process tensor data sets, tensor-based match pursuit is formulated for multi-dimension sparse signal recovery, in which atom vectors orthogonality selection strategy and basis-signal reevaluation are used to eliminate the wrong indices and enhance resolution respectively. Simulation results validates that the proposed method can complete high-resolution imaging correctly compared with conventional greedy sparse recovery algorithms. Additionally, under fewer snapshots condition, RMSE of proposed method is far lower than other sparse recovery algorithms.
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spelling doaj.art-1ae0f98a293349f68ddf0db2d75a9d6e2022-12-21T22:38:48ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122018-06-01272580586Tensor-Based Match Pursuit Algorithm for MIMO Radar ImagingP. HuangX. LiH. WangIn MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered simultaneously and a new compressive sensing imaging algorithm named tensor-based match pursuit(TMP) is proposed. In the proposed method, MIMO radar tensor signal model is established to eliminate “dimension disaster” at first. Then, exploiting tensor decomposition to process tensor data sets, tensor-based match pursuit is formulated for multi-dimension sparse signal recovery, in which atom vectors orthogonality selection strategy and basis-signal reevaluation are used to eliminate the wrong indices and enhance resolution respectively. Simulation results validates that the proposed method can complete high-resolution imaging correctly compared with conventional greedy sparse recovery algorithms. Additionally, under fewer snapshots condition, RMSE of proposed method is far lower than other sparse recovery algorithms.https://www.radioeng.cz/fulltexts/2018/18_02_0580_0586.pdfMIMO radarCompressive sensing (CS)tensor decompositionsparse imaginggreedy algorithm
spellingShingle P. Huang
X. Li
H. Wang
Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
Radioengineering
MIMO radar
Compressive sensing (CS)
tensor decomposition
sparse imaging
greedy algorithm
title Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
title_full Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
title_fullStr Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
title_full_unstemmed Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
title_short Tensor-Based Match Pursuit Algorithm for MIMO Radar Imaging
title_sort tensor based match pursuit algorithm for mimo radar imaging
topic MIMO radar
Compressive sensing (CS)
tensor decomposition
sparse imaging
greedy algorithm
url https://www.radioeng.cz/fulltexts/2018/18_02_0580_0586.pdf
work_keys_str_mv AT phuang tensorbasedmatchpursuitalgorithmformimoradarimaging
AT xli tensorbasedmatchpursuitalgorithmformimoradarimaging
AT hwang tensorbasedmatchpursuitalgorithmformimoradarimaging