TY - JOUR
T1 - Joint Design of SAR Waveform and Imaging Filters Based on Target Information Maximization
AU - Zhang, Jiawei
AU - Xu, Huaping
AU - Liu, Wei
AU - Li, Chunsheng
AU - Feng, Liang
AU - Chen, Yifan
N1 - Publisher Copyright:
© 2007-2012 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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.
AB - 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.
KW - Constrained optimization
KW - SAR
KW - imaging filter
KW - information acquisition
KW - transmit waveform
UR - https://www.scopus.com/pages/publications/85141483072
U2 - 10.1109/JSTSP.2022.3217895
DO - 10.1109/JSTSP.2022.3217895
M3 - 文章
AN - SCOPUS:85141483072
SN - 1932-4553
VL - 17
SP - 416
EP - 430
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 2
ER -