基于回波序列最小二乘拟合的高分辨率SAR运动目标速度估计  被引量:5

Velocity Estimation of Moving Targets Based on Least Square Fitting of High-resolution SAR Echo Sequences

在线阅读下载全文

作  者:王超[1,2] 王岩飞 王琦[1] 詹学丽[1] WANG Chao;WANG Yanfei;WANG Qi;ZHAN Xueli(Institute of Electronics,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区:[1]中国科学院电子学研究所,北京100190 [2]中国科学院大学,北京100049

出  处:《电子与信息学报》2019年第5期1055-1062,共8页Journal of Electronics & Information Technology

基  金:国家重点研发计划(2017YFB0503001);国家自然科学基金(61471340)~~

摘  要:运动目标速度估计是机载单天线高分辨率合成孔径雷达(SAR)实现运动目标成像和定位的关键环节。针对现有方法运算量大、易受距离徙动干扰等缺点,该文提出一种基于回波序列最小二乘拟合的速度估计方法。利用该方法,首先通过包络相关提取相邻回波序列的距离变化量,然后对其做最小二乘线性拟合,目标的距离向速度和方位向速度可由拟合系数计算得到。与传统方法相比,该方法不仅计算量小,而且无须先做距离徙动校正(RCMC)。该文给出了新方法的数学模型和参数选取原则,分析了该方法的估计精度、计算量和适用条件,并通过仿真和实际数据处理验证了该方法的有效性。Velocity estimation of moving targets is a key part of ground moving target imaging and positioning in airborne single-antenna high-resolution SAR system.In order to solute the defects of traditional algorithms, such as high computation brought by searching and interpolation and low reliability caused by range cell migration,a novel method based on least square fitting of echo sequence is proposed.Range changes between adjacent echo sequences are extracted using envelope correlation,and coefficients of range change equation are obtained by least square linear fitting,from which radial velocity and along-track velocity can be derived. Compared with the traditional algorithms,the new method has less computation and can work without RCMC. The mathematical model is presented and the principle of parameter selection is provided,and accuracy, computation and applicable conditions of the algorithm are analyzed.The effectiveness of the proposed algorithm is validated by simulation and real data.

关 键 词:合成孔径雷达 运动目标成像 速度估计 回波序列 最小二乘拟合 

分 类 号:TN957.51[电子电信—信号与信息处理]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象