数据缺失情况下基于M⁃SPICE的低空风切变风速估计方法

李 海; 许 婷; $2; 张 强

引用本文:	李海，许婷，严忠平，张强. 数据缺失情况下基于M⁃SPICE的低空风切变风速估计方法[J]. 雷达科学与技术, 2023, 21(5): 473-481.[点击复制]
	LI Hai, XU Ting, YAN Zhongping, ZHANG Qiang. Estimation of Windshear Wind Speed at Low⁃Altitude Based on M⁃SPICE with Missing Data[J]. Radar Science and Technology, 2023, 21(5): 473-481.[点击复制]

【打印本页】【下载PDF全文】【查看/发表评论】【下载PDF阅读器】【关闭】

←前一篇|后一篇→

过刊浏览高级检索

本文已被：浏览 5141次下载 491次	码上扫一扫！
分享到：微信更多字体:加大+\|默认\|缩小-
数据缺失情况下基于M⁃SPICE的低空风切变风速估计方法
李海，许婷，严忠平，张强
1. 中国民航大学天津市智能信号与图像处理重点实验室，天津 300300;2. 中国航空工业集团公司雷华电子技术研究所，江苏无锡 214063

摘要:

针对机载气象雷达回波数据缺失的情况下，低空风切变风速估计失准这一问题，本文提出了一种基于缺失数据稀疏迭代协方差估计（Missing Sparse Iterative Covariance?based Estimation，M?SPICE）的低空风切变风速估计方法。该方法首先构造数据缺失模型，然后根据协方差拟合准则计算估计算子，并不断迭代更新得到最终所需的估计算子，进而恢复得到缺失的风切变数据，最后将恢复得到的数据重构得到完整的风切变数据，实现对风场速度的准确估计。仿真结果表明，该方法能够有效实现缺失数据的重建并精确地估计风速。

关键词: 机载气象雷达缺失数据稀疏迭代协方差估计低空风切变风速估计

DOI：DOI:10.3969/j.issn.1672-2337.2023.05.001

分类号:TN959.4

基金项目:国家重点研发计划项目（No.2021YFB1600600）；民机项目（No.MJ?2018?S?28）；中央高校基本科研业务费项目（No.3122015B002）；中国民航大学蓝天教学名师培养经费

Estimation of Windshear Wind Speed at Low⁃Altitude Based on M⁃SPICE with Missing Data

LI Hai, XU Ting, YAN Zhongping, ZHANG Qiang

1. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China;2. AVIC Leihua Electronic Technology Research Institute, Wuxi 214063, China

Abstract:

Aiming at the problem of inaccurate wind speed estimation of low?altitude windshear in the absence of airborne weather radar echo data, a missing sparse iterative covariance?based estimation （M?SPICE） method for low?altitude wind speed estimation is proposed in this paper. This method first constructs the data missing model, then calculates the estimation operator according to the covariance fitting criterion, and iteratively updates the final estimation ope?rator to recover the missing windshear data, and finally reconstructs the recovered data to obtain the complete windshear data, so as to achieve the accurate estimation of wind field velocity. The experimental results show that the method can reconstruct the missing data effectively and estimate the wind speed accurately.

Key words: airborne weather radar missing sparse iterative covariance⁃based estimation low⁃altitude wind⁃shear wind speed estimation