基于多域联合直达波估计的建筑布局层析成像方法

李念; 刘杰; 于君明; 朱智豪; 刘剑光; 郭世盛; 陈家辉; 崔国龙; 孔令讲; 杨晓波

doi:10.12000/JR24220

基于多域联合直达波估计的建筑布局层析成像方法

DOI: 10.12000/JR24220

1.
电子科技大学信息与通信工程学院成都 611731
2.
中国电子科技集团公司第二十七研究所郑州 450047

基金项目: 国家自然科学基金(62401098, 62371110)

详细信息

作者简介:
李念，博士生，主要研究方向为穿墙雷达成像和射频无线电层析成像

刘杰，研究员，主要研究方向为航天测控与卫星应用

于君明，研究员，主要研究方向为SAR成像处理及遥感应用

朱智豪，博士生，主要研究方向为阵列信号处理与非直视目标探测技术

刘剑光，高工，主要研究方向为计算机软件应用

郭世盛，博士，研究员，主要研究方向为城市环境目标探测、基于雷达的人体行为识别等

陈家辉，博士，师资博士后，主要研究方向为雷达非直视隐蔽探测

崔国龙，博士，教授，主要研究方向为最优化理论和算法、雷达目标检测理论、波形多样性以及城市环境目标探测等

孔令讲，博士，教授，主要研究方向为新体制雷达、统计信号处理、优化理论和算法、雷达信号处理、非合作信号处理技术和自适应阵列信号处理及城市环境目标探测等

杨晓波，教授，主要研究方向为雷达隐蔽探测与成像、太赫兹雷达等

通讯作者:
陈家辉 chenjiahui@uestc.edu.cn

责任主编：杨小鹏 Corresponding Editor: YANG Xiaopeng
中图分类号: TN957.52
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出版历程
- 收稿日期: 2024-11-06

Building Layout Tomography Method Based on Joint Multidomain Direct Wave Estimation

1.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
The 27th Research Institute of China Electronics Technology Group Corporation, Zhengzhou 450047, China

Funds: The National Natural Science Foundation of China (62401098, 62371110)

More Information

Corresponding author: CHEN Jiahui, chenjiahui@uestc.edu.cn

摘要

摘要: 在进入陌生建筑物之前掌握其内部结构信息，能够为反恐作战、灾害救援、监视管控等多种应用提供支持，具有重要的现实意义和研究价值。为实现建筑布局结构信息获取，该文开展了基于多域联合直达波估计的建筑布局层析成像方法研究。首先，建立了线性近似模型，实现了直达波信号传播时延与未知建筑布局图像之间的映射关系；在此模型基础上，分析了在层析成像模式下直达波信号与多径信号在快时间域、慢时间域与多普勒域中的分布特性，提出了一种基于多域联合的直达波估计算法，实现了多径干扰抑制与直达波信号精确估计；此外，提出了一种总变分约束的投影矩阵自适应修正代数重建算法，提升了有限数据下的建筑布局反演质量；最后，电磁仿真与实测实验结果证明了所提出的建筑布局层析成像方法的有效性，其重建结果的结构相似性指标分别可达到91.2%和81.7%，显著优于现有建筑布局层析成像方法。
- 建筑布局层析成像 /
- 直达波估计 /
- 代数重建技术 /
- 总变分约束 /
- 投影矩阵自适应修正
Abstract: Obtaining internal layout information before entering unfamiliar buildings is crucial for various applications, such as counter-terrorism operations, disaster relief, and surveillance, highlighting its great practical significance and research value. To enable the acquisition of the building layout information, this paper presents a building layout tomography method based on joint multidomain direct wave estimation. First, a linear approximation model is established to map the relationship between the propagation delay of direct wave signals and the layout of the unknown building. Using this model, the distribution characteristics of direct wave and multipath signals in the fast-time, slow-time, and Doppler domains are analyzed in the tomographic imaging mode. A joint multidomain direct wave estimation algorithm is then proposed to achieve the suppression of multipath interference and precise estimation of direct wave signals. Additionally, a projection matrix adaptive correction algebraic reconstruction algorithm with total variation constraints is proposed, which enhances building layout inversion quality under limited data scenarios. Finally, electromagnetic simulation and experimental results demonstrate the effectiveness of the proposed building layout tomography method, with structural similarity indices of 91.2% and 81.7% for the reconstructed results, significantly outperforming existing building layout tomography methods.

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图 1 射频层析成像示意图

Figure 1. Schematic diagram of RTI

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图 2 线性层析投影模型示意图

Figure 2. Schematic diagram of linear tomographic projection model

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图 3 直达波与多径信号对比示意图

Figure 3. Comparison diagram of propagation time between direct wave and multipath propagation

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图 4 第96个采样点脉冲压缩结果

Figure 4. The 96th sampling point pulse compression result

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图 5 第91～101个采样点R-D谱结果

Figure 5. R-D spectrum results of sampling points 91th～101th

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图 6 多径抑制结果

Figure 6. Multipath suppression results

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图 7 仿真场景图

Figure 7. Simulation scene diagram

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图 8 0°路径观测数据估计结果

Figure 8. Observation data estimation results of 0 ° path

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图 9 仿真场景重建结果

Figure 9. Simulation scene reconstruction results

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图 10 合成孔径成像结果

Figure 10. Synthetic aperture imaging results

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图 11 –10 dB～10 dB信噪比下不同方法重建结果对比

Figure 11. Comparison of reconstruction results using different methods at SNR ranging from –10 dB to 10 dB

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图 12 实测场景图

Figure 12. Actual scene diagram

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图 13 实测场景重建结果

Figure 13. Real scene reconstruction results

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表 1 PMAM-ART-TV算法流程

Table 1. PMAM-ART-TV algorithm Flow

输入： P, A，初始化$ {\boldsymbol{O}}=0 $，外部停止标准${\varepsilon _o}$，内部停止标准　${\varepsilon _i}$，外部迭代次数$t = 0$，外部最大迭代次数$ {T_o} $，内部最大迭代　次数${T_i}$
输出: $ \tilde{{\boldsymbol{O}}}={\boldsymbol{O}}^{t} $
repeat
1、代数重建迭代：
$k = 0$
求解式(16)、式(17)更新$ \Delta C_{}^{k + 1} $
求解式(18)更新$ {\boldsymbol{O}}_n^{k + 1} $
$ k = k + 1 $
直到$k = {T_i}$或者$ \Vert {{\boldsymbol{O}}}^{k+1}-{{\boldsymbol{O}}}^{k}\Vert \le {\varepsilon }_{i} $，输出$ {{{\boldsymbol{O}}}_{ART}} $
2、总变分约束迭代：
$k = 0$,$ {{\boldsymbol{O}}} = {{{\boldsymbol{O}}}_{ART}} $, $ {{{\boldsymbol{u}}}^k} = 0 $
求解式(23)更新$ {{{\boldsymbol{u}}}^{k + 1}} $
求解式(24)更新${{\boldsymbol{b}}}_x^{k + 1}$, ${{\boldsymbol{b}}}_y^{k + 1}$
求解式(25)更新$ {{\boldsymbol{d}}}_x^{k + 1} $, $ {{\boldsymbol{d}}}_y^{k + 1} $
$ {{\boldsymbol{O}}} = {{{\boldsymbol{u}}}^{k + 1}} $, $ k = k + 1 $
直到$k = {T_i}$或者$ \Vert {{\boldsymbol{O}}}^{k+1}-{{\boldsymbol{O}}}^{k}\Vert \le {\varepsilon }_{i} $，输出O
3、更新投影矩阵
求解式(28)更新A, $ {{{\boldsymbol{O}}}^{t + 1}} $, $t = t + 1$
until直到$t = {T_o}$或者$ \Vert {{\boldsymbol{O}}}^{t+1}-{{\boldsymbol{O}}}^{t}\Vert \le {\varepsilon }_{o} $

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表 2 仿真参数

Table 2. Simulation parameters

类型	仿真参数	数值
信号参数	发射信号	步进频信号
	中心频率	1.5 GHz
	带宽	600 MHz
	单频点持续时间	100 us
扫描参数	采样路径长度	10 m
	采样路径数目	4组
	采样间隔	0.05 m
场景参数	场景尺寸	2 m×2 m
	墙体厚度	0.20 m
	相对介电常数	4
	电导率	0.01 S/m

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表 3 观测数据误差对比

Table 3. Comparison of observation data errors

观测数据	RMSE
RSSI^[15]	0.367
MAE^[18]	0.219
MD-DE	0.138

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表 4 仿真成像结果SSIM指标对比

Table 4. Comparison of SSIM indicators in simulation imaging results

	ART	Tikhonov	TV	PMAM-ART-TV
RSSI	30.8%	28.7%	48.9%	68.5%
MAE	34.1%	51.4%	72.8%	86.2%
MD-DE	40.6%	58.9%	88.9%	91.2%

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表 5 雷达系统参数

Table 5. Radar system parameters

参数名称	数值
中心频率	1.9 GHz
带宽	600 MHz
频率步进	2 MHz
频点持续时间	100 μs

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表 6 实测成像结果SSIM指标对比

Table 6. Comparison of SSIM indicators in actual imaging results

	ART	Tikhonov	TV	PMAM-ART-TV
RSSI	31.4%	30.6%	31.2%	31.7%
MAE	43.9%	49.8%	73.6%	75.5%
MD-DE	46.3%	56.6%	78.6%	81.7%

下载: 导出CSV

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