Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge.
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| Title: | Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge. |
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| Authors: | Wang, Hongyan1 gglongs@163.com, Pei, Bingnan1 peibingnan@163.com, Li, Jun2 Junli01@mail.xidian.edu.cn |
| Source: | Circuits, Systems & Signal Processing. Apr2016, Vol. 35 Issue 4, p1239-1255. 17p. |
| Subjects: | MIMO radar, Performance of MIMO systems, Wave analysis, Covariance matrices, Analysis of covariance |
| Abstract: | Waveform optimization for multi-input multi-output radar usually depends on the initial parameter estimates (i.e., some prior information on the target of interest and scenario). However, it is sensitive to estimate errors and uncertainty in the parameters. Robust waveform design attempts to systematically alleviate the sensitivity by explicitly incorporating a parameter uncertainty model into the optimization problem. In this paper, we consider the robust waveform optimization to improve the worst-case performance of parameter estimation over a convex uncertainty model, which is based on the Cramer-Rao bound. An iterative algorithm is proposed to optimize the waveform covariance matrix such that the worst-case performance can be improved. Each iteration step in the proposed algorithm is solved by resorting to convex relaxation that belongs to the semidefinite programming class. Numerical results show that the worst-case performance can be improved considerably by the proposed method compared to that of uncorrelated waveforms and the non-robust method. [ABSTRACT FROM AUTHOR] |
| Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Hongyan%22">Wang, Hongyan</searchLink><relatesTo>1</relatesTo><i> gglongs@163.com</i><br /><searchLink fieldCode="AR" term="%22Pei%2C+Bingnan%22">Pei, Bingnan</searchLink><relatesTo>1</relatesTo><i> peibingnan@163.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jun%22">Li, Jun</searchLink><relatesTo>2</relatesTo><i> Junli01@mail.xidian.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Circuits%2C+Systems+%26+Signal+Processing%22">Circuits, Systems & Signal Processing</searchLink>. Apr2016, Vol. 35 Issue 4, p1239-1255. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22MIMO+radar%22">MIMO radar</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+of+MIMO+systems%22">Performance of MIMO systems</searchLink><br /><searchLink fieldCode="DE" term="%22Wave+analysis%22">Wave analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Covariance+matrices%22">Covariance matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+covariance%22">Analysis of covariance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Waveform optimization for multi-input multi-output radar usually depends on the initial parameter estimates (i.e., some prior information on the target of interest and scenario). However, it is sensitive to estimate errors and uncertainty in the parameters. Robust waveform design attempts to systematically alleviate the sensitivity by explicitly incorporating a parameter uncertainty model into the optimization problem. In this paper, we consider the robust waveform optimization to improve the worst-case performance of parameter estimation over a convex uncertainty model, which is based on the Cramer-Rao bound. An iterative algorithm is proposed to optimize the waveform covariance matrix such that the worst-case performance can be improved. Each iteration step in the proposed algorithm is solved by resorting to convex relaxation that belongs to the semidefinite programming class. Numerical results show that the worst-case performance can be improved considerably by the proposed method compared to that of uncorrelated waveforms and the non-robust method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00034-015-0116-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1239 Subjects: – SubjectFull: MIMO radar Type: general – SubjectFull: Performance of MIMO systems Type: general – SubjectFull: Wave analysis Type: general – SubjectFull: Covariance matrices Type: general – SubjectFull: Analysis of covariance Type: general Titles: – TitleFull: Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Hongyan – PersonEntity: Name: NameFull: Pei, Bingnan – PersonEntity: Name: NameFull: Li, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 0278081X Numbering: – Type: volume Value: 35 – Type: issue Value: 4 Titles: – TitleFull: Circuits, Systems & Signal Processing Type: main |
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