崔宁^{1, 2},
行坤¹,
段克清^3,,,
喻忠军^{1, 2,,}
1.中国科学院空天信息创新研究院 北京 100094
2.中国科学院大学电子电气与通信工程学院 北京 100049
3.中山大学电子与通信工程学院 广州 510006
基金项目:国家自然科学基金(61871397)

详细信息

作者简介:崔宁：崔　宁(1995–)，男，辽宁抚顺人，中国科学院大学在读博士研究生，主要研究方向为空时自适应处理和雷达动目标检测等
行坤：行　坤(1980–)，男，陕西西安人，中国科学院空天信息创新研究院副研究员，中国科学院大学硕士生导师，主要研究方向为机载多功能雷达、地基监视雷达系统总体设计与性能仿真，地面、海面、空中动目标检测与跟踪技术等
段克清(1981–)，男，河北石家庄人，中山大学副教授，硕士生导师，主要研究方向为阵列信号处理、空时自适应处理、压缩感知、深度学习及其在雷达系统中的应用
喻忠军(1980–)，男，四川成都人，中国科学院空天信息创新研究院研究员，中国科学院大学博士生导师，主要研究方向为微系统微集成、微波毫米波电路模块、先进相控阵天馈技术等

通讯作者:段克清 duankeqing@aliyun.com
喻忠军 yuzj@ucas.ac.cn

责任主编：冯大政 Corresponding Editor: FENG Dazheng
中图分类号:TN957.51

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被引次数:0

出版历程

收稿日期:2021-09-26
录用日期:2021-12-06
修回日期:2021-12-03
网络出版日期:2021-12-07
刊出日期:2021-12-28

Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method

CUI Ning^{1, 2},
XING Kun¹,
DUAN Keqing^3,,,
YU Zhongjun^{1, 2,,}
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China
Funds:The National Natural Science Foundation of China (61871397)

More Information

Corresponding author:DUAN Keqing, duankeqing@aliyun.com;YU Zhongjun, yuzj@ucas.ac.cn

摘要
摘要:当机载雷达处于非正侧视工作模式时，非平稳杂波会对运动目标检测造成严重干扰。传统三维空时自适应处理(3D-STAP)方法通过构造俯仰-方位-多普勒三维自适应滤波器，可有效抑制非平稳杂波，然而巨大的系统自由度导致其在非均匀杂波环境下训练样本严重不足。虽然稀疏恢复(SR)技术可有效改善样本需求，但庞大的运算开销又使得该技术难以应用于实际。针对上述问题，该文结合机载雷达回3阶张量结构提出一种新的快速三维稀疏贝叶斯学习STAP方法，通过采用运算开销更低的张量处理将大规模矩阵求解拆分为多个小规模矩阵计算，从而大幅降低运算复杂度。详尽的数值实验验证了所提张量基SR-STAP方法可在维持SR-STAP小样本处理性能不变的基础上，将运行时间直接降低数个量级，因此是一种更适用于实际工程的SR-STAP处理方式。
关键词:三维空时自适应处理/
稀疏恢复/
机载雷达/
非平稳杂波/
张量结构
Abstract:When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering.
Key words:Three-dimensional Space-Time Adaptive Processing (3D-STAP)/
Sparse Recovery (SR)/
Airborne radar/
Non-stationary clutter/
Tensor structure



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