Zhang, J. orcid.org/0000-0002-6439-8834, Xu, H. orcid.org/0000-0002-9559-3691, Liu, W. orcid.org/0000-0003-2968-2888 et al. (3 more authors) (2023) Joint design of SAR waveform and imaging filters based on target information maximization. IEEE Journal of Selected Topics in Signal Processing, 17 (2). pp. 416-430. ISSN 1932-4553
Abstract
In this paper, a joint design of both transmit waveform and imaging filters for synthetic aperture radar (SAR) is proposed to improve its information acquisition capability. First, the mutual information between SAR image and target scattering characteristics is considered as the performance metric, and its equivalent analytical version is derived in the 2-D frequency domain. The design is formulated as an optimization problem with an energy condition and a similarity constraint. Then, to tackle the resultant non-convex problem, by referring to the Dinkelbach's method, an algorithm is derived to find the desired solution via a cyclic maximization procedure alternating between three subproblems. Based on minorization-maximization, a unified optimization method with an increasing penalty on constraint violation is proposed to solve all subproblems. Convergence of the developed algorithm is analytically proved. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed design.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Synthetic aperture radar; Signal to noise ratio; Optimization; Radar polarimetry; Radar; Imaging; Radar imaging; SAR; transmit waveform; imaging filter; information acquisition; constrained optimization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Feb 2023 11:41 |
Last Modified: | 25 Sep 2024 15:54 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/jstsp.2022.3217895 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196057 |