陈伯孝2
1.南昌工程学院信息工程学院 南昌 310099
2.西安电子科技大学雷达信号处理国家重点实验室 西安 710071
基金项目:国家自然科学基金(61401187)，江西省教育厅科学技术研究项目(GJJ170990)
详细信息
作者简介:陈根华：男，1980年生，副教授，博士，研究方向为阵列雷达信号处理
陈伯孝：男，1966年生，教授，博士生导师，研究方向为新体制雷达系统设计及其实现、雷达信号处理、目标精确制导与跟踪等
通讯作者:陈根华 cghnit@126.com
中图分类号:TN958计量
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被引次数:0
出版历程
收稿日期:2019-07-24
修回日期:2020-02-24
网络出版日期:2020-03-21
刊出日期:2020-06-04
Robust Altitude Estimation Based on Spatial Sign Transform in the Presence of Diffuse Multipath for Very High Frequency Radar
Genhua CHEN1,,,Baixiao CHEN2
1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 310099, China
2. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Funds:The National Natural Science Foundation of China(61401187), The Science Research of Jiangxi Provincial Department of Education(GJJ170990)
摘要
摘要:针对米波(VHF)雷达的复杂多径信号中散射分量的非高斯性严重影响测高的稳定性，该文提出了稳健的空域符号变换最大似然测高算法。该算法先对多维阵列快拍矢量进行空域符号变换处理，以抑制散射分量野值点对阵列协方差矩阵及其测高算法的影响，再计算符号协方差矩阵(SCM)，然后根据符号协方差矩阵的映射等效性和特征空间不变性，将符号协方差矩阵应用到最大似然(SCM-ML)测高算法中，实现了稳健的米波雷达低角测高。该算法有效抑制了多径信号中散射分量和波束打地形成的强杂波的非高斯性，提高了米波雷达低角测高的稳健性。仿真结果和实测数据验证了算法的稳健性与有效性。
关键词:米波雷达/
空域符号变换/
散射分量/
非高斯性/
测高
Abstract:A robust spatial sign transform-based maximum likelihood method for low-elevation target altitude measurement is proposed in the presence of the non-Gaussian diffuse multipath component for Very High Frequency (VHF) radar. The spatial sign transform is implemented to the antenna array snapshots, reducing the influence of the outliers on array covariance matrix and the low elevation estimation algorithms, followed by computing the spatial Sign Covariance Matrix(SCM). Then the application of SCM to the Maximum Likelihood method(SCM-ML) is presented on the basis of the affine equivalence and preservation of the eigenstructure for robust low elevation estimation and height finding of VHF radar. The proposed method effectively solves the non-Gaussian property of the diffuse multipath component and improves the robustness of low elevation estimation. Simulation result and real data demonstrate the robustness and validation of the SCM-ML method.
Key words:Very High Frequency (VHF) radar/
Spatial sign transform/
Diffuse component/
Non-Gaussian/
Altitude estimation
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