Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing

Handle URI:
http://hdl.handle.net/10754/622689
Title:
Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing
Authors:
Ali, Hussain; Ahmed, Sajid; Al-Naffouri, Tareq Y.; Sharawi, Mohammad S.; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Ali H, Ahmed S, Al-Naffouri TY, Sharawi MS, Alouini M-S (2017) Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing. EURASIP Journal on Advances in Signal Processing 2017. Available: http://dx.doi.org/10.1186/s13634-016-0436-x.
Publisher:
Springer Nature
Journal:
EURASIP Journal on Advances in Signal Processing
KAUST Grant Number:
KAUST-002
Issue Date:
9-Jan-2017
DOI:
10.1186/s13634-016-0436-x
Type:
Article
ISSN:
1687-6180
Sponsors:
This research was funded by a grant from the office of competitive research funding (OCRF) at the King Abdullah University of Science and Technology (KAUST). The work was also supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, through project number KAUST-002. The authors acknowledge the Information Technology Center at King Fahd University of Petroleum and Minerals (KFUPM) for providing high performance computing resources that have contributed to the research results reported within this paper.
Additional Links:
http://asp.eurasipjournals.springeropen.com/articles/10.1186/s13634-016-0436-x
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAli, Hussainen
dc.contributor.authorAhmed, Sajiden
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorSharawi, Mohammad S.en
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2017-01-12T13:01:28Z-
dc.date.available2017-01-12T13:01:28Z-
dc.date.issued2017-01-09en
dc.identifier.citationAli H, Ahmed S, Al-Naffouri TY, Sharawi MS, Alouini M-S (2017) Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing. EURASIP Journal on Advances in Signal Processing 2017. Available: http://dx.doi.org/10.1186/s13634-016-0436-x.en
dc.identifier.issn1687-6180en
dc.identifier.doi10.1186/s13634-016-0436-xen
dc.identifier.urihttp://hdl.handle.net/10754/622689-
dc.description.abstractConventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.en
dc.description.sponsorshipThis research was funded by a grant from the office of competitive research funding (OCRF) at the King Abdullah University of Science and Technology (KAUST). The work was also supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, through project number KAUST-002. The authors acknowledge the Information Technology Center at King Fahd University of Petroleum and Minerals (KFUPM) for providing high performance computing resources that have contributed to the research results reported within this paper.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://asp.eurasipjournals.springeropen.com/articles/10.1186/s13634-016-0436-xen
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectCompressive sensingen
dc.subjectMIMO radaren
dc.subjectColocateden
dc.titleTarget parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalEURASIP Journal on Advances in Signal Processingen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionElectrical Engineering Department, KFUPM, Dhahran, Saudi Arabiaen
kaust.authorAhmed, Sajiden
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorAlouini, Mohamed-Slimen
kaust.grant.numberKAUST-002en
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