基于多角度双层偏差度量的封闭空间SAR多径抑制方法

林赟; 赵甲萌; 王彦平; 李洋; 申文杰; 白泽朝; 蒋雯

doi:10.12000/JR24076

基于多角度双层偏差度量的封闭空间SAR多径抑制方法

DOI: 10.12000/JR24076

北方工业大学信息学院雷达监测技术实验室北京 100144

基金项目: 国家自然科学基金(62131001, 62371005)，北京市教育委员会创新团队建设计划项目(IDHT20190501)

详细信息

作者简介:
林　赟，副教授，研究方向为智能雷达成像技术与图像处理和信息提取技术

赵甲萌，硕士生，研究方向为SAR成像技术和多径抑制方法

王彦平，教授，研究方向为雷达成像安全监测技术，遥感智能信息处理及应用

李　洋，副教授，研究方向为机器学习、传感器定位与构图，多目标航迹生成

申文杰，讲师，研究方向为SAR信号处理，包括成像、动目标检测和参数估计等

白泽朝，讲师，研究方向为星载、地基InSAR技术理论和应用

蒋　雯，讲师，研究方向为雷达智能感知与对抗、雷达系统仿真

通讯作者:
王彦平 wangyp@ncut.edu.cn

责任主编：毕辉 Corresponding Editor: BI Hui
中图分类号: TN95
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出版历程
- 收稿日期: 2024-04-28
- 修回日期: 2024-06-23
- 网络出版日期: 2024-07-10
- 刊出日期: 2024-08-28

Closed Space SAR Multipath Suppression Method Based on Multi-angle Dual-layer Deviation Measurement

Radar Monitoring Technology Laboratory, School of Information, North China University of Technology, Beijing 100144, China

Funds: The National Natural Science Foundation of China (62131001, 62371005), The Innovation Team Building Support Program of the Beijing Municipal Education Commission (IDHT20190501)

More Information

Corresponding author: WANG Yanping, wangyp@ncut.edu.cn

摘要

摘要: 合成孔径雷达(SAR)具有全天时全天候非接触式监测的优势，是封闭空间安全监测的重要工具。然而，SAR应用于复杂封闭空间时，易受多径效应影响，导致图像存在大量虚像，严重影响判读。现有方法需要场景先验进行多径推算或通过子孔径加权融合抑制多径，但都难以准确区分多径虚像与目标图像。该文提出了一种新的多角度双层偏差度量方法，可有效获取多径虚像与目标间的特征差异。该方法首先采用大视角差对观测场景进行多角度观测，可充分利用多径虚像位置随观测角度变化，而实际目标位置保持不变的特性。然后使用双层偏差度量算法，该算法根据多径在多角度序列中的稀疏性，两次计算序列幅度值与序列均值的偏差，精准检测出稀疏、不稳定的多径成分并去除，对剩余稳定成分取均值。这样，在保留目标信息的同时有效抑制多径。最后，仿真和毫米波雷达实际数据处理验证了该文方法的有效性。
- 毫米波雷达 /
- 多径抑制 /
- 多角度观测 /
- 双层偏差度量 /
- 封闭空间
Abstract: Synthetic Aperture Radar (SAR) has the advantage of noncontact monitoring around the clock and is an important tool for closed space security monitoring. However, when SAR is employed in complex closed spaces, it is susceptible to multipath effects, resulting in a considerable number of virtual images in the image, which has a detrimental impact on interpretation. Existing methods require scene priors for multipath estimation or subaperture weighted fusion to suppress multipath; however, accurately distinguishing multipath virtual images from target images is challenging. This paper proposes a novel multi-angle dual-layer deviation measurement method that effectively distinguishes multipath virtual images from targets. The proposed method employs a large viewing angle difference to conduct multi-angle observation of the target scene, capitalizing on the fact that the position of the multipath virtual image varies with the observation angle, whereas the actual target position remains constant; this is followed by applying a dual-layer deviation measurement algorithm. The algorithm calculates the deviation between the sequence amplitude value and mean twice based on the sparsity of multipath in the multiangle sequence. The proposed method accurately detects and removes sparse and unstable multipath components, whereas the remaining stable components are averaged. This effectively suppresses multipath while retaining target information. Finally, the simulation and actual millimeter wave radar data processing verified the effectiveness of the proposed method.
- Millimeter wave radar /
- Multipath suppression /
- Multi-angle observation /
- Dual-layer deviation /
- Closed space

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图 1 多角度观测几何模型

Figure 1. Multi-angle observation geometric model

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图 2 多径信号模型

Figure 2. Multipath signal model

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图 3 多角度序列融合算法示意图

Figure 3. Schematic diagram of multi-angle sequence fusion algorithm

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图 4 双层阈值偏差算法示意图

Figure 4. Schematic diagram of dual-layer threshold deviation algorithm

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图 5 仿真1实验处理结果

Figure 5. Simulation 1 experiment processing results

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图 6 算法中间结果可视化图

Figure 6. Visualization diagram of algorithm intermediate results

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图 7 噪声对算法的影响

Figure 7. The impact of noise on the algorithm

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图 8 仿真2实验处理结果

Figure 8. Simulation 2 experiment processing results

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图 9 实验1处理结果

Figure 9. Experiment 1 processing results

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图 10 实验2处理结果

Figure 10. Experiment 2 processing results

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1 多角度序列融合算法

1. Multi-angle sequence fusion algorithm

输入：P_c, P_t
输出：sum_vector
1 // Phase 1: process P_c
2 for i = 1 : length(P_c) do
3 　$\mu $= mean(P_c[i]); //calculate average
4 　${\delta _f}$ = \|P_c[i]－$\mu $\|; //calculate deviations
5 　$\overline \delta $ = mean(${\delta _f}$); //calculate deviations metric
6 　validvalues = {x∈P_c[i] \| x－$\mu $≤$\overline \delta $};
7 　if validvalues ≠ 0 then
8 　　$P'_c $[i] = mean(validvalues);
9 　else
10 　 $P'_c $[i] = 0;
11 end
12 end
13 // Phase 2: process P_t
14 $P'_t $ = mean(P_t,2);
15 // Phase 3: sequence fusion
16 $ P'_{ct} $ = $P'_c $ + $P'_t $;
17 sum_vector = reshape($P'_{ct} $).

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表 1 仿真参数

Table 1. Simulation parameters

参数	数值
中心频率	77 GHz
带宽	600 MHz
方位向采样点数	2048
距离向采样点数	1024
轨道长度	1.2 m
频率间隔	585.94 kHz
距离分辨率	0.2498 m
孔径长度	0.1 m

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表 2 10个角度下多径虚像的位置坐标(m)

Table 2. Position coordinates of multipath virtual image at ten angles (m)

角度	C₁	C₂	C₁₂
角度1	(0.28, 5.67)	(0.29, 8.49)	(5.90, 6.11)
角度2	(0.43, 5.69)	(0.44, 8.50)	(5.87, 6.13)
角度3	(0.58, 5.69)	(0.59, 8.51)	(5.80, 6.17)
角度4	(0.70, 5.71)	(0.73, 8.52)	(5.75, 6.21)
角度5	(0.82, 5.72)	(0.88, 8.53)	(5.67, 6.26)
角度6	(0.92, 5.75)	(1.01, 8.55)	(5.59, 6.13)
角度7	(1.00, 5.77)	(1.13, 8.57)	(5.50, 6.35)
角度8	(1.07, 5.79)	(1.25, 8.59)	(5.41, 6.39)
角度9	(1.10, 5.80)	(1.35, 8.60)	(5.32, 6.42)
角度10	(1.12, 5.81)	(1.44, 8.63)	(5.22, 6.46)

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表 3 实验参数

Table 3. Experimental parameters

参数	数值
载频	77 GHz
发射天线数量	2
接收天线数量使用通道数	4 1
带宽	514.14 MHz
ADC采样率	12500 ksps
总采集时间	120 s
轨道长度	1.2 m
帧数目	60000
采样点数	512
单帧时长	2 ms
子孔径长度	0.1 m

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表 4 TCR性能对比

Table 4. TCR performance comparison

方法	实验1	实验2
双层子孔径融合法	1.796	14.194
标准差度量法	2.407	22.725
中心向量阈值法	2.481	24.887
本文方法	3.096	27.162

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