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极化敏感线阵的模值约束降维Root-MUSIC算法
引用本文:王炜彤, 杨健, 郭晓冉, 等. 基于压缩感知的正交偶极子阵列信号参数估计[J]. 中国舰船研究, 2022, 17(1): 221–226, 234. doi: 10.19693/j.issn.1673-3185.02262
作者姓名:王炜彤  杨健  郭晓冉  刘鲁涛
作者单位:1.哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;2.先进船舶通信与信息技术工业和信息化部重点实验室, 黑龙江 哈尔滨 150001;3.北京遥感设备研究所, 北京 100854;4.中国人民解放军32181部队, 河北 石家庄 050000;5.
基金项目:国家自然科学基金资助项目(61571146,61801143);中央高校基本科研业务费专项资金资助(3072020CF0815)
摘    要:  目的  针对传统的极化敏感阵列的波达方向(DOA)估计算法运算复杂度高、实时性差的问题,提出基于压缩感知的正交偶极子极化敏感阵列结构。  方法  将数据压缩思想应用于阵列结构设计,压缩接收信号矢量维度,减少射频前端链路数量,以控制系统的复杂度,使阵列结构设计具有高度的灵活性。基于结构降维多重信号分类(MUSIC)算法;首先,通过空间谱搜索实现信号的DOA估计;然后,利用拉格朗日乘数法降维;最后,通过解决优化问题获取信号的极化参数信息。  结果  仿真实验表明:采用所提阵列结构及方法在入射信号完全极化且非相干时,可以获得正确的信号DOA和极化参数联合估计;在信噪比(SNR)大于10 dB的环境下,俯仰角均方根误差(RMSE)低于0.05°。  结论  与相同条件下同等通道数的非压缩结构相比,基于压缩感知的正交偶极子阵列参数估计结构的估计精度更高、运算复杂度更低。

关 键 词:极化敏感阵列  正交偶极子  波达方向估计  MUSIC算法
收稿时间:2021-01-08
修稿时间:2021-04-07

On the performance of a polarization sensitive adaptive array
WANG W T, YANG J, GUO X R, et al. Joint estimation for DOA and polarization parameters of orthogonal dipole array based on compressive sensing[J]. Chinese Journal of Ship Research, 2022, 17(1): 221–226, 234. doi: 10.19693/j.issn.1673-3185.02262
Authors:WANG Weitong  YANG Jian  GUO Xiaoran  LIU Lutao
Affiliation:1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;2.MIIT Key Laboratory of Advanced Marine Communication and Information Technology, Harbin 150001, China;3.Beijing Institute of Remote Sensing Equipment, Beijing 100854, China;4.The 32181 Unit of PLA, Shijiazhuang 050000, China
Abstract:  Objectives  As the detection-of-arrival (DOA) estimation algorithm used in traditional polarization sensitive arrays has such problems as high computation complexity and poor real-time performance, this study proposes a data compression-based orthogonal dipole polarization sensitive array structure.   Methods  By applying compression sensing technology to the system design (i.e., data compression technology), the proposed structure compresses the dimensions of the receiving signal vector, controls the complexity of the system by reducing the number of front-end chains, and brings high flexibility to the array structure design. At the same time, a dimensionality reduction-based multiple signal classification (MUSIC) algorithm is also proposed. First, the DOA estimation of signals is realized through spatial spectrum searching. The Lagrange multiplier method is then used to reduce the searching dimensionality, and the signal polarization parameters are obtained by solving the optimization problem.  Results  Simulation experiments show that the proposed array structure and MUSIC algorithm can correctly estimate DOA and polarization parameters when the incident signals are completely polarized and incoherent. When the signal-noise ratio (SNR) is greater than 10 dB, the root mean square error (RMSE) of the elevation angle is less than 0.05°.   Conclusions  Compared with the non-compressed structure with an equal channel number under the same conditions, the proposed structure can provide higher estimation accuracy and lower computational complexity.
Keywords:polarization sensitive array  orthogonal dipole  detection-of-arrival estimation  multiple signal classification algorithm
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