﻿ 基于参数估计的海面运动舰船SAR成像方法
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 雷达学报  2016, Vol. 5 Issue (3): 326-332  DOI: 10.12000/JR15104 0

### 引用本文 [复制中英文]

[复制中文]
Yun Yajiao, Qi Xiangyang and Li Ning. Moving ship SAR imaging based on parameter estimation[J]. Journal of Radars, 2016, 5(3): 326-332. DOI: 10.12000/JR15104.
[复制英文]

### 文章历史

, ,
(中国科学院电子学研究所 北京 100190)
(中国科学院大学 北京 100039)

Moving Ship SAR Imaging Based on Parameter Estimation
, ,
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
(University of Chinese Academy of Sciences, Beijing 100039, China)
Foundation Item: The National Ministries Foundation
Abstract: The Doppler parameters of moving targets affect the conventional Synthetic Aperture Radar (SAR) imaging. In this study, the relation between the motion and Doppler parameters is established. With improved popular technology, a set of moving ship SAR imaging processes is proposed to obtain a focused and rightlocated image. Simulations and experimental data are used to verify the method.
Key words: Synthetic Aperture Radar (SAR)     Moving target     Maritime ship imagining
1 引言

2 运动目标信号分析

SAR回波信号经过检波和距离压缩后，可表示为：
 $s(t,\tau ) = \sigma \exp \left( { - {\rm{j2}}\pi {f_{\rm{0}}}\tau } \right)$ (1)

 图 1 SAR几何模型 Fig. 1 The geometric model of SAR

 $R(t) = \sqrt {{{({y_0} + {v_y}t)}^2} + {{({x_0} + {v_x}t - {v_{\rm{a}}}t)}^2}}$ (2)

 $\begin{array}{*{20}{l}} {R(t - {t_{\rm{c}}}) = {y_{\rm{c}}} + {v_y}(t - {t_{\rm{c}}})}\\ {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + \frac{{{{({v_{\rm{a}}} - {v_x})}^2}}}{{2{y_{\rm{c}}}}}{{(t - {t_{\rm{c}}})}^2}} \end{array}$ (3)

 $\begin{array}{*{20}{l}} {s\left( {t - {t_{\rm{c}}},\tau } \right) = \sigma \exp \left\{ { - {\rm{j}}\frac{{{\rm{4}}\pi }}{\lambda }\left[{{y_{\rm{c}}} + {v_y}\left( {t - {t_{\rm{c}}}} \right)} \right.} \right.}\\ {\left. {\left. {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + \frac{{{{\left( {{v_{\rm{a}}} - {v_x}} \right)}^2}}}{{2{y_{\rm{c}}}}}{{\left( {t - {t_{\rm{c}}}} \right)}^2}} \right]} \right\}} \end{array}$ (4)

 $f = - \frac{{\rm{2}}}{\lambda }\left[{{v_y} + \frac{{{{({v_{\rm{a}}} - {v_x})}^2}}}{{{y_{\rm{c}}}}}(t - {t_{\rm{c}}})} \right]$ (5)

3 多普勒参数与运动目标速度的估计

3.1 多普勒中心频率与径向速度的估计

 $M = {\rm{round}}\left( {\frac{{{f_{\rm{e}}} - {{f'}_{{\rm{tc}}}}}}{{{\rm{PRF}}}}} \right)$ (6)

 ${f_{{\rm{tc}}}} = {f'_{{\rm{tc}}}} + M \times {\rm{PRF}}$ (7)

 $\Delta R(t - {t_{\rm{c}}}) = {v_y}(t - {t_{\rm{c}}}) + \frac{{{{({v_{\rm{a}}} - {v_x})}^2}}}{{2{y_{\rm{c}}}}}{(t - {t_{\rm{c}}})^2}$ (8)

3.2 多普勒调频率与方位向速度的估计

 图 2 不同调频率对应的熵 Fig. 2 The entropy of different Doppler frequency rate

 图 3 调频率估计流程 Fig. 3 Flow chart of Doppler frequency rate estimation
4 算法流程

 图 4 距离徙动 Fig. 4 Range migration

 $\begin{array}{*{20}{l}} {\Delta {R_t}({f_{\rm{a}}}) = \frac{{{\lambda ^2}{y_{\rm{c}}}}}{{{\rm{8}}{{({v_{\rm{a}}} - {v_x})}^2}}}f_{\rm{a}}^2 - \frac{{\lambda {y_{\rm{c}}}{v_y}}}{{{\rm{2}}{{({v_{\rm{a}}} - {v_x})}^2}}}{f_{\rm{a}}}}\\ {\;\;\;\;\;\;\;\;\;\;\;\; = \frac{{{\lambda ^2}{y_{\rm{c}}}}}{{{\rm{8}}{{({v_{\rm{a}}} - {v_x})}^2}}}f_{\rm{a}}^2 - \frac{{\lambda {y_{\rm{c}}}{v_y}}}{{{\rm{2}}{{({v_{\rm{a}}} - {v_x})}^2}}}{f_{\rm{a}}}}\\ {\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + \frac{{{y_{\rm{c}}}{v_y}^2}}{{2{{({v_{\rm{a}}} - {v_x})}^2}}} - \frac{{{y_{\rm{c}}}{v_y}^2}}{{2{{({v_{\rm{a}}} - {v_x})}^2}}}}\\ {\;\;\;\;\;\;\;\;\;\;\;\; = \frac{{{\lambda ^2}{y_{\rm{c}}}}}{{8{{({v_{\rm{a}}} - {v_x})}^2}}}{{({f_{\rm{a}}} - {f_{{\rm{tc}}}})}^2} - \frac{{{y_{\rm{c}}}v_{\rm{y}}^2}}{{2{{({v_{\rm{a}}} - {v_x})}^2}}}} \end{array}$ (9)

5 仿真与实测数据成像结果及分析

 图 5 点目标信号质量分析 Fig. 5 Quality analysis of point target

 图 6 本文算法成像效果 Fig. 6 The results by using this article imaging algorithm

 图 7 实测数据处理结果 Fig. 7 The imaging of measured data

6 结论