多角度探测模式下结合Hough变换与SVR的墙后目标定位算法

欧阳方平; 曹家璇; 丁一鹏

doi:10.12000/JR23236

多角度探测模式下结合Hough变换与SVR的墙后目标定位算法

DOI: 10.12000/JR23236

1.
中南大学物理学院长沙 410012
2.
中南大学电子信息学院长沙 410004

基金项目: 湖南省自然科学基金(2022JJ30749)，中南大学研究生自主探索创新项目(2023ZZTS0398)，国家自然科学基金(52073308)，湖南省创新省建设专项基金 (2020RC3004)

详细信息

作者简介:
欧阳方平，教授，博士生导师，主要研究方向为低维量子材料与器件物理、计算凝聚态物理和纳米电子学

曹家璇，硕士生，主要研究方向为墙体参数估计、时频分析技术和机器学习

丁一鹏，博士，教授，博士生导师，主要研究方向为雷达信号处理

通讯作者:
丁一鹏 dingyipeng@sina.com

责任主编：郭世盛 Corresponding Editor: GUO Shisheng
中图分类号: TN957.52
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出版历程
- 收稿日期: 2023-11-30
- 修回日期: 2024-01-21
- 网络出版日期: 2024-01-31
- 刊出日期: 2024-08-28

A Through-wall Target Location Algorithm Combing Hough Transform and SVR in Multi-view Detection Mode

1.
School of Physics, Central South University, Changsha 410012, China
2.
School of Electronic Information, Central South University, Changsha 410004, China

Funds: The Natural Science Foundation of Hunan Province (2022JJ30749), The Fundamental Research Funds for the Central Universities of Central South University (2023ZZTS0398), The National Natural Science Foundation of China (52073308) and the Special Foundation for Hunan Innovation Province Construction (2020RC3004)

More Information

Corresponding author: DING Yipeng, dingyipeng@sina.com

摘要

摘要: 多普勒穿墙雷达在定位墙后目标时，存在以下两个难点：(1)准确获取频率混叠区域目标瞬时频率；(2)通过获取精确的墙体参数来减小墙体对定位造成的影响。针对以上问题该文提出了一种结合Hough变换和支持向量回归-BP神经网络的目标定位算法。该文首先设计了一种多视角融合穿墙目标探测模型框架，通过获取不同视角下的目标位置来提供辅助估计墙体参数信息；其次，结合差分进化算法和切比雪夫插值多项式提出了一种目标瞬时频率曲线的高精度提取和估计算法；最后，利用估计的墙体参数信息，提出了一种基于BP神经网络的目标运动轨迹补偿算法，抑制了障碍物对目标定位结果的扭曲影响，实现了对墙后目标的精确定位。实验结果表明，相较于传统的短时傅里叶方法，该文所述方法可以准确提取时频混叠区域的目标瞬时频率曲线并减小墙体造成的影响，从而实现墙后多目标的准确定位，整体定位精度提升了约85%。
- 多普勒穿墙雷达 /
- 目标定位 /
- Hough变换 /
- 切比雪夫多项式 /
- 运动轨迹补偿
Abstract: Doppler through-wall radar faces two challenges when locating targets concealed behind walls: (1) precisely determining the instantaneous frequency of the target within the frequency aliasing region and (2) reducing the impact of the wall on positioning by determining accurate wall parameters. To address these issues, this paper introduces a target localization algorithm that combines the Hough transform and support vector regression-BP neural network. First, a multiview fusion model framework is proposed for through-wall target detection, which enables the auxiliary estimation of wall parameter information by acquiring target positions from different perspectives. Second, a high-precision extraction and estimation algorithm for the instantaneous frequency curve of the target is proposed by combining the differential evolutionary algorithm and Chebyshev interpolation polynomials. Finally, a target motion trajectory compensation algorithm based on the Back Propagation (BP) neural network is proposed using the estimated wall parameter information, which suppresses the distorting effect of obstacles on target localization results and achieves the accurate localization of the target behind a wall. Experimental results indicate that compared with the conventional short-time Fourier method, the developed algorithm can accurately extract target instantaneous frequency curves within the time-frequency aliasing region. Moreover, it successfully reduces the impact caused by walls, facilitating the precise localization of multiple targets behind walls, and the overall localization accuracy is improved ～85%.
- Doppler through-wall radar /
- Target localization /
- Hough transform /
- Chebyshev polynomials /
- Target trajectory compensation

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图 1 传统多普勒雷达探测模型

Figure 1. Traditional Doppler radar target detection model

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图 2 多普勒穿墙雷达目标定位原理图

Figure 2. Doppler through-wall radar localization schematic

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图 3 多视角融合穿墙目标探测模型

Figure 3. Multi-view fusion through-wall target detection model

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图 4 BPNN结构图

Figure 4. BPNN structure diagram

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图 5 基于切比雪夫插值多项式的Hough变换瞬时频率估计算法与基于SVR-BPNN的墙体厚度估计与运动轨迹补偿算法流程图

Figure 5. Flowchart of Hough transform instantaneous frequency estimation algorithm based on Chebyshev’s multi-interpolation term formulation and SVR-BPNN based wall thickness estimation and motion trajectory compensation algorithm

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图 6 训练与测试所用的目标运动轨迹

Figure 6. The target motion trajectories used for training and testing

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图 7 实验设备与场景

Figure 7. The experimental equipment and scenarios

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图 8 模拟实验中的墙体厚度估计结果

Figure 8. The estimated results of wall thickness in the simulated experiments

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图 9 无墙场景下的目标频率估计与定位结果

Figure 9. Target frequency estimation and localization results in no wall scenes

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图 10 墙后目标定位结果

Figure 10. The localization results of targets behind the wall

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表 1 雷达系统参数设置

Table 1. Radar system parameters settings

参数	数值
载波频率 f_c1, f_c2 (GHz)	2.40, 2.39
最大/最小发射功率P_max, P_min (dBm)	30, 15
天线增益G (dBi)	3.5
天线带宽B (MHz)	40
天线间隔d (m)	0.06
采样频率(Hz)	200
最大方位角θ_m (°)	75

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表 2 STFT、二次贝塞尔模型、四阶切比雪夫插值多项式模型误差对比(无墙双目标场景)

Table 2. Algorithm errors comparison of STFT, quadratic Bezier model and 4th order Chebyshev interpolating polynomial model (scene of dual target without walls)

算法	目标1频率(Hz)	目标1定位(m)	目标2频率(Hz)	目标2定位(m)
STFT	0.17	0.16	0.16	0.31
基于二次贝塞尔模型的Hough变换	0.07	0.13	0.10	0.57
基于四阶切比雪夫插值多项式的Hough变换	0.04	0.07	0.07	0.09

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表 3 STFT、二次贝塞尔模型、轨迹相交法、四阶切比雪夫插值多项式模型误差对比(墙后双目标场景)

Table 3. Algorithm errors comparison of STFT, quadratic Bezier model, trajectory intersection method and 4th order Chebyshev interpolating polynomial model (scene of dual target behind a wall)

算法	砖墙场景		混凝土墙场景
算法	目标1定位(m)	目标2定位(m)	目标1定位(m)	目标2定位(m)
STFT	0.59	0.33	0.70	0.77
基于二次贝塞尔模型的Hough变换	0.68	0.66	0.81	0.79
轨迹相交法	0.27	0.22	0.29	0.25
基于四阶切比雪夫插值多项式的Hough变换	0.10	0.14	0.09	0.13

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