基于概率模型驱动的机载贝叶斯前视超分辨多目标成像方法

陈洪猛; 余继周; 张文杰; 李亚超; 李军; 蔡良; 鲁耀兵

doi:10.12000/JR23080

基于概率模型驱动的机载贝叶斯前视超分辨多目标成像方法

DOI: 10.12000/JR23080

陈洪猛¹,
余继周^1, ,,
张文杰¹,
李亚超²,
李军¹,
蔡良¹,
鲁耀兵^1, ,

1.
北京无线电测量研究所北京 100854
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(62101396, 62171337)

详细信息

作者简介:
陈洪猛，高级工程师，主要研究方向为空天基雷达总体设计、前斜视成像和运动目标检测

余继周，研究员，主要研究方向为雷达总体设计、雷达成像和目标识别

张文杰，高级工程师，主要研究方向为空天基雷达总体设计

李亚超，教授，博士生导师，研究方向为合成孔径雷达/逆合成孔径雷达成像、弹载SAR成像、地面运动目标检测(GMTI)、SAR图像的匹配和定向、基于现场可编程门阵列(FPGA)和数字信号处理(DSP)技术的实时信号处理以及分布式雷达

李　军，研究员，主要研究方向为空天基雷达总体设计、雷达成像和目标检测

蔡　良，研究员，主要研究方向为雷达总体设计、雷达探测与成像

鲁耀兵，研究员，主要研究方向为雷达总体设计、雷达探测与成像

通讯作者:
余继周 2917161774@qq.com

鲁耀兵 luyaobing65@163.com

责任主编：代大海 Corresponding Editor: DAI Dahai
中图分类号: TN957
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- 被引次数: 15
出版历程
- 收稿日期: 2023-05-09
- 修回日期: 2023-08-15
- 网络出版日期: 2023-09-06
- 刊出日期: 2023-12-28

Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario

1.
Beijing Institute of Radio Measurement, Beijing 100854, China
2.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62101396, 62171337)

More Information

Corresponding author: YU Jizhou, 2917161774@qq.com; LU Yaobing, luyaobing65@163.com

摘要

摘要: 雷达前视成像技术在精确制导打击、自主下降着陆、汽车自动驾驶等军民领域具有广阔的应用前景。由于多普勒相位历程的限制，机载平台的前视成像分辨率较低。解卷积方法可以进行前视成像，但当前视成像场景复杂时，现有的前视成像方法的成像质量会下降。针对复杂前视成像构型下的场景稀疏度度量和表征问题，该文提出一种基于概率模型驱动的机载贝叶斯前视超分辨多目标成像方法。首先通过将前视成像场景的数据维度由单帧空间扩展到多帧空间提升场景的稀疏度，然后基于广义高斯概率模型对成像场景的稀疏特性进行统计建模和稀疏度求解，最后基于贝叶斯框架完成稀疏前视成像。由于选取的稀疏度表征参数嵌入到前视成像的整个过程中，在每次迭代期间都会进行前视成像参数的更新，从而保证了前视成像算法的稳健性。通过计算机结果和实测数据处理，验证了该文方法的有效性。
- 前视成像 /
- 超分辨成像 /
- 稀疏特性 /
- 概率模型 /
- 多普勒解卷积
Abstract: Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.
- Forward-looking imaging /
- Super-resolution imaging /
- Sparsity /
- Probability model /
- Doppler deconvolution

HTML全文

图 1 机载前视成像雷达工作示意图

Figure 1. Geometry of airborne forward-looking radar

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图 2 某一距离单元的直方图分布统计

Figure 2. Histogram distribution for one range cell

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图 3 真实的点目标分布场景

Figure 3. True point targets distribution scene

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图 4 不同方法的前视成像结果对比

Figure 4. Angular super-resolution results of different methods

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图 5 不同SNR下的仿真点目标RMSE变化曲线

Figure 5. RMSE curves of simulation point targets under different SNRs

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图 6 真实成像场景

Figure 6. True original scene of surface targets

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图 7 不同方法的多目标前视成像结果对比

Figure 7. Angular super-resolution results of different methods for multitarget scenario

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图 8 不同SNR下的仿真面目标RMSE变化曲线

Figure 8. RMSE curves of simulation surface targets under different SNRs

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图 9 不同方法的实测数据前视成像结果对比

Figure 9. Angular super-resolution results of different methods for real data

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图 10 不同方法的前视成像结果局部放大对比图

Figure 10. Zoomed in results of different methods

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表 1 点目标仿真实验雷达参数

Table 1. Radar parameters for simulation experiment with point targets

参数	数值	参数	数值
平台速度(m/s)	300	时宽(μs)	10
平台高度(m)	1000	方位波束(°)	3
带宽(MHz)	512	扫描范围(°)	–15～15

下载: 导出CSV

1 前视超分辨成像方法求解流程

1. Solution flow of the proposed algorithm

输入：天线方向图矩阵H，扩展的多普勒卷积矩阵 ${\boldsymbol{\varPhi}}$ ，观测数　　　　据Y
初始化：迭代次数 $m = 1$ ， ${\hat {\boldsymbol{X} }^1} = {\boldsymbol{Y}}$
更新迭代过程：
(1) 更新p：　 $p = {F^{ { { - } }1} }\left( {\frac{ { { {\left( {\dfrac{1}{M}\displaystyle\sum\limits_{i = 1}^M {\left\| { {X_i} } \right\|} } \right)}^2} } }{ {\dfrac{1}{M}{ {\displaystyle\sum\limits_{i = 1}^M {\left( { {X_i} - \dfrac{1}{M}\displaystyle\sum\limits_{i = 1}^M {\left\| { {X_i} } \right\|} } \right)} }^2} } } } \right)$ , 　 $F\left( x \right) = \dfrac{ { {\Gamma ^2}\left( { {2 \mathord{\left/ {\vphantom {2 p} } \right. } p} } \right)} }{ {\Gamma \left( { {1 \mathord{\left/ {\vphantom {1 p} } \right. } p} } \right)\Gamma \left( { {3 \mathord{\left/ {\vphantom {3 p} } \right. } p} } \right)} }$
(2) 更新 ${\boldsymbol{J}}\left( { { {\hat {\boldsymbol{X}}}^n} } \right)$ ：
${\boldsymbol{J} }\left( { { {\hat {\boldsymbol{X} } }^n} } \right) = \arg {\text{min} }\left\{ {\left\\| { {\boldsymbol{Y} }{ { - } }{\boldsymbol{H} } \odot {\boldsymbol{\varPhi} } { {\boldsymbol{X} }^n} } \right\\|_2^2 + \mu { {\displaystyle\sum\limits_{i = 1}^L {\left( {\left\| { {x^n} } \right\|_i^2 + \varepsilon } \right)} }^{\textstyle\frac{p}{2} } } } \right\}$
(3) 更新 $\nabla {\boldsymbol{J}}\left( { { {\hat {\boldsymbol{X}}}^n} } \right)$ ：
$\nabla {\boldsymbol{J} }\left( { { {\hat {\boldsymbol{X} } }^n} } \right) = \left[ {2{ {\left( { {\boldsymbol{H} } \odot {\boldsymbol{\varPhi} } } \right)}^{\text{H} } }{\boldsymbol{H} } \odot {\boldsymbol{\varPhi}} + \mu {\boldsymbol{\varLambda } }\left( { { {\hat {\boldsymbol{X} } }^n} } \right)} \right]{\boldsymbol{X} } - 2{\left( { {\boldsymbol{H} } \odot {\boldsymbol{\varPhi} } } \right)^{\text{H} } }{\boldsymbol{Y} }$
(4) 更新 ${A^n}$ ：
${A^n}{\text{ = } }2{\left( {{\boldsymbol{H}} \odot {\boldsymbol{\varPhi}} } \right)^{\text{H} } }{\boldsymbol{H}} \odot {\boldsymbol{\varPhi}} + \mu {\boldsymbol{\varLambda } }$
(5) 更新 ${\hat {\boldsymbol{X}}^n}$ ：
${\hat {\boldsymbol{X} }^{n + 1} } = {\hat {\boldsymbol{X} }^n} - {\left[ {A\left( { { {\hat {\boldsymbol{X} } }^n} } \right)} \right]^{ { { - } }1} } \cdot \nabla {\boldsymbol{J}}\left( { { {\hat {\boldsymbol{X} } }^n} } \right)$
输出：图像矩阵 ${\hat {\boldsymbol{X}}^{n + 1} }$

下载: 导出CSV

表 2 面目标仿真实验雷达参数

Table 2. Radar parameters for simulation experiment with surface targets

参数	数值	参数	数值
平台速度(m/s)	300	时宽(μs)	10
平台高度(m)	1000	方位波束(°)	3
带宽(MHz)	300	扫描范围(°)	–15～15

下载: 导出CSV

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