Joint Design of Power Allocation and Unimodular Waveform for Polarimetric Radar

被引:3
作者
Zhong, Kai [1 ]
Hu, Jinfeng [2 ,3 ]
Li, Huiyong [1 ]
Wang, Yuankai [4 ]
Cheng, Xin [1 ]
Cheng, Xu [5 ]
Pan, Cunhua [6 ]
Teh, Kah Chan [7 ]
Cui, Guolong [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Quzhou 324000, Zhejiang, Peoples R China
[3] Yibin Inst UESTC, Intelligent Terminal Key Lab Sichuan Prov, Yibin 644000, Peoples R China
[4] 41st Inst CETC, Qingdao 266555, Peoples R China
[5] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan 430205, Peoples R China
[6] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 211189, Peoples R China
[7] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2025年 / 63卷
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Radar; Resource management; Radar polarimetry; Clutter; Signal to noise ratio; Scattering; Manifolds; Optimization; Linear programming; Covariance matrices; Adaptive unified manifold optimization (AUMO); polarization radar; power allocation; receive filter; waveform design; MIMO RADAR; TRANSMIT; OPTIMIZATION;
D O I
10.1109/TGRS.2024.3516834
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
Polarization adds an additional dimension to the radar signals, contributing to waveform diversity. Codesign of unimodular waveforms and filters with polarimetric power allocation for maximizing the signal-to-interference-plus-noise ratio (SINR) plays a key role in the polarimetric radar system. The problem is challenging to solve due to the nonconvex nature of the objective function and constraints, coupled with the interdependence of multiple variables. Existing methods mainly solve this problem by fixing the power allocation or relaxing the objective function and obtaining the receive filters with matrix inversion. We directly address this problem without matrix inversion by using the proposed adaptive unified manifold optimization (AUMO) framework. Specifically, a unified manifold space (UMS) is constructed to satisfy the constraints of unimodular waveform, filters, and power, transforming the problem to an unconstrained optimization problem over the manifold. To solve this problem, a parallel conjugate gradient (PCG) algorithm is derived. This algorithm can adaptively change the step size by exploring the local features of the manifold space. The experimental results based on the measured data show that the proposed method outperforms existing methods in terms of SINR gain and execution time.
引用
收藏
页数:12
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