Joint Design of Frequency and Bandwidth for Multifrequency SAR Based on Mutual Information Maximization

被引:0
|
作者
Xu, Huaping [1 ]
Zhou, Zihan [1 ]
Liu, Wei [2 ]
Zhang, Jiawei [3 ]
Li, Wei [4 ]
机构
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[2] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Kowloon, Hong Kong, Peoples R China
[3] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Peoples R China
[4] Shanghai Inst Satellite Engn, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Object detection; Bandwidth; Radar polarimetry; Spaceborne radar; Radar; Receivers; Radar imaging; frequency; genetic algorithm (GA); mutual information (MI); synthetic aperture radar (SAR); target detection; SYNTHETIC-APERTURE RADAR; WAVE-FORM DESIGN; WIDE-BAND;
D O I
10.1109/TGRS.2024.3439742
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The carrier frequency and bandwidth are vital parameters of radar transmit signals. In this article, a joint design of frequency and bandwidth in multifrequency synthetic aperture radar (SAR) is proposed to improve the target information acquisition capability. First, the target detection mutual information (MI) is considered as the performance metric, and its mathematical expression is derived using the example of decision-level fusion and hypothesis testing. The design is formulated as an optimization problem with multiple engineering constraints based on MI maximization. Then, a modified genetic algorithm (GA) is proposed to find the optimal solution satisfying the constraints via a code adjustment operator. It is shown by simulation results that for a specific scene, the multifrequency SAR with its frequencies and bandwidths designed by the proposed method has higher target information acquisition capability and more accurate target detection performance than existing spaceborne SAR systems.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Joint Design of SAR Waveform and Imaging Filters Based on Target Information Maximization
    Zhang, Jiawei
    Xu, Huaping
    Liu, Wei
    Li, Chunsheng
    Feng, Liang
    Chen, Yifan
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2023, 17 (02) : 416 - 430
  • [2] SAR Incremental Automatic Target Recognition Based on Mutual Information Maximization
    Li, Bin
    Cui, Zongyong
    Wang, Haohan
    Deng, Yijie
    Ma, Jizhen
    Yang, Jianyu
    Cao, Zongjie
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21 : 1 - 5
  • [3] Feature selection based on fuzzy joint mutual information maximization
    Salem, Omar A. M.
    Liu, Feng
    Sherif, Ahmed Sobhy
    Zhang, Wen
    Chen, Xi
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2020, 18 (01) : 305 - 327
  • [4] FREQUENCIES DESIGN METHOD OF MULTI-FREQUENCY SAR BASED ON MAXIMUM MUTUAL INFORMATION CRITERION
    Zhou, Zihan
    Xu, Huaping
    Zhang, Jiawei
    Li, Chunsheng
    Li, Wei
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 7707 - 7710
  • [5] Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal Means Based on a Space-Frequency Information Joint Covariance Matrix
    Ma, Xiaoshuang
    Wu, Penghai
    Shen, Huanfeng
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (01) : 274 - 284
  • [6] Signal estimation based on mutual information maximization
    Rohde, G. K.
    Nichols, J.
    Bucholtz, F.
    Michalowicz, J. V.
    CONFERENCE RECORD OF THE FORTY-FIRST ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1-5, 2007, : 597 - +
  • [7] Multiuser Multihop MIMO Relay System Design Based on Mutual Information Maximization
    He, Zhiqiang
    Guo, Sichuan
    Ou, Yuanbiao
    Rong, Yue
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (21) : 5725 - 5733
  • [8] A hierarchical clustering based on mutual information maximization
    Aghagolzadeh, M.
    Soltanian-Zadeh, H.
    Araabi, B.
    Aghagolzadeh, A.
    2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, : 277 - +
  • [9] Medical image segmentation based on mutual information maximization
    Rigau, J
    Feixas, M
    Sbert, M
    Bardera, A
    Boada, I
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 1, PROCEEDINGS, 2004, 3216 : 135 - 142
  • [10] Deep node clustering based on mutual information maximization
    Molaei, Soheila
    Bousejin, Nima Ghanbari
    Zare, Hadi
    Jalili, Mahdi
    NEUROCOMPUTING, 2021, 455 : 274 - 282