中国人民解放军信息工程大学数据与目标工程学院,河南郑州 450001
黎庞哲 男,2001年9月出生于重庆市。现为中国人民解放军网络空间部队信息工程大学数据与目标工程学院硕士研究生。主要研究方向为空间目标信号获取与处理。E-mail: J_kui@outlook.com
胡德秀 男,2017年博士毕业于清华大学电子工程系。现为中国人民解放军网络空间部队信息工程大学数据与目标工程学院副教授。主要研究方向为无源定位与电子对抗。E-mail: paper_hdx@126.com
崔毓函 女,1996年10月出生于上海市。现为中国人民解放军网络空间部队信息工程大学数据与目标工程学院讲师。主要研究方向为无人机网络资源调度及其应用。E-mail: 497781293@qq.com
赵闯 男,1978年出生于河北省。现为中国人民解放军网络空间部队信息工程大学数据与目标工程学院副教授。主要研究方向为电子对抗、雷达信号处理。E-mail: rushzhao@163.com
杨阳 男,1990年3月出生于河南省。博士毕业于西安电子科技大学信号与信息处理专业。现为河南省安全苛求系统人机智能交互工程技术研究中心讲师。主要研究方向为深度学习、人工智能、信号处理。
收稿:2026-03-27,
录用:2026-04-24,
纸质出版:2026-06-25
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黎庞哲, 胡德秀, 崔毓函, 等. 海空目标部分极化雷达回波动态建模与实验验证[J]. 电子学报, 2026, 54(06): 2790-2804.
LI Pangzhe, HU Dexiu, CUI Yuhan, et al. Dynamic Modeling and Experimental Verification of Partially Polarized Radar Returns from Maritime and Aerial Targets[J]. Acta Electronica Sinica, 2026, 54(06): 2790-2804.
黎庞哲, 胡德秀, 崔毓函, 等. 海空目标部分极化雷达回波动态建模与实验验证[J]. 电子学报, 2026, 54(06): 2790-2804. DOI:10.12263/DZXB.20260252
LI Pangzhe, HU Dexiu, CUI Yuhan, et al. Dynamic Modeling and Experimental Verification of Partially Polarized Radar Returns from Maritime and Aerial Targets[J]. Acta Electronica Sinica, 2026, 54(06): 2790-2804. DOI:10.12263/DZXB.20260252
极化信息是雷达目标识别与特征提取的重要参数。现有对极化信号的研究多聚焦于全极化信号,然而当雷达照射海面舰船或空中机动目标时,目标姿态的连续变化、多散射中心干涉以及时变环境杂波等因素耦合作用,使得回波往往呈现部分极化特性,其极化状态并非恒定,而是随时间不断演化,蕴含丰富的目标物理信息。当前对实际雷达回波中广泛存在的部分极化信号的分析仅限于静态统计描述,缺乏对其时变极化状态动态演化机理的建模与分析。针对上述问题,本文建立了一种适用于海空动态目标的统一动态极化状态模型。该模型将接收信号分解为全极化分量与动态非极化分量,并引入基于随机微分方程的目标姿态动态模型、极化参数‑姿态角非线性耦合模型以及双尺度非极化功率模型,构建了统一表征海面目标低频周期运动与空中目标高频机动特性的动态极化状态框架。基于此框架,理论推导了时变极化度与斯托克斯参数的统计特性及谱结构,进而揭示目标微动与散射起伏是驱动其极化状态演化的关键物理机理。实测数据的验证结果表明,所提模型能够有效模拟海空目标部分极化信号的动态极化行为,其极化度起伏与谱特征和实测数据基本一致,验证了模型在揭示目标微动与散射机制方面的有效性。本文所建立的模型为动态部分极化信号的特征提取与目标识别提供了一个机理清晰、可解释性强的统一建模框架,有助于从回波信号中定量反演目标的物理状态。
Polarization information is a crucial parameter in radar target recognition and feature extraction. Existing researche on polarized signals mainly focuses on fully polarized signals. Nevertheless
when radar irradiates sea surface ships or aerial maneuvering targets
the coupled effects of continuous target attitude variation
multi-scattering center interference and time-varying environmental clutter endow radar echoes with inherent partially polarized characteristics. The polarization state of such echoes is time-varying rather than constant
and contains abundant physical information of targets. At present
the analysis of widely existing partially polarized signals in actual radar echoes is limited to static statistical description
while the modeling and analysis of dynamic evolution mechanism of time-varying polarization state are insufficient. To address this insufficiency in dynamic characterization
this paper aims to establish a unified dynamic polarization state model applicable to maritime and aerial dynamic targets. The model decomposes the received signal into a fully polarized component and a dynamically fluctuating unpolarized component. By introducing a target attitude dynamic model based on stochastic differential equations
a nonlinear coupling model between polarization parameters and attitude angles
and a dual-scale unpolarized power model
a unified dynamic polarization state framework is constructed. This framework simultaneously accommodates the low-frequency periodic motion of maritime targets and the high-frequency maneuvering characteristics of aerial targets. Based on this framework
the statistical properties and spectral structure of the time-varying degree of polarization and Stokes parameters are theoretically derived
revealing that target micromotion and scattering fluctuations are key physical mechanisms driving the evolution of the polarization state. Validation results against measured data demonstrate that the proposed model effectively simulates the dynamic polarization behavior of partially polarized signals from maritime and aerial targets. The fluctuations in the degree of polarization and spectral features of the simulated signals show good consistency with the measured data
verifying the model’s effectiveness in revealing target micromotion and scattering mechanisms. The model established in this work provides a unified modeling framework with clear mechanisms and strong interpretability for feature extraction and target recognition using dynamic partially polarized signals
facilitating the quantitative inversion of target physical states from radar returns.
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