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Trajectory Planning Method for UAV-through-the-wall 3D SAR Based on a Genetic Algorithm
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摘要: 传统手持或车载穿墙雷达由于架设高度受限，无法对城市高层建筑内部目标进行透视成像，无人机载穿墙雷达具有灵活机动、高效便捷、无高度限制等优势，可对城市高层楼宇进行大范围三维穿透探测。三维层析合成孔径雷达(SAR)成像广泛采用多基线扫描模式，以获得高度向高分辨能力，但存在高度向空域欠采样导致的栅瓣问题。对此，该文提出一种基于遗传算法的无人机载穿墙三维SAR航迹规划方法，通过非均匀化飞行航迹，削弱周期性的雷达回波能量叠加，从而抑制栅瓣、实现更优的成像质量。该算法结合飞行距离与无人机载穿墙雷达成像质量的内在关系，建立了无人机航迹规划代价函数；利用遗传算法对3种典型的无人机飞行轨迹关键控制点进行基因编码，并进行基因杂交、变异等以优化种群与个体；最终通过最小化代价函数，分别筛选出3种典型飞行模式下的最优飞行航迹。仿真和实测数据的三维成像结果表明：相较于传统等间距多基线飞行模式，所提方法显著抑制了成像目标的栅瓣效应；此外，无人机斜线飞行的航迹长度明显缩短，提高了无人机载穿墙SAR成像效率。Abstract: Due to height limitations, the traditional handheld or vehicle-mounted Through-the-Wall Radar (TWR) cannot provide the perspective imaging of internal targets in urban high-rise buildings. Unmanned Aerial Vehicle-TWR (UAV-TWR) offers flexibility, efficiency, convenience, and no height limitations, allowing for large-scale three-Dimensional (3D) penetration detection of urban high-rise buildings. While the multibaseline scanning mode is widely used in 3D tomographic Synthetic Aperture Radar (SAR) imaging to provide resolution in the altitude direction, it often suffers from the grating lobe problem owing to under-sampling in the altitude spatial domain. Therefore, this paper proposes a trajectory planning algorithm for UAV-through-the-wall 3D SAR imaging based on a genetic algorithm to address this issue. By nonuniformizing flight trajectories, the periodic radar echo energy superposition is weakened, thereby suppressing grating lobes to achieve better imaging quality. The proposed algorithm combines the inherent relationship between the flight distance and TWR imaging quality and establishes a cost function for UAV-TWR trajectory planning. We use the genetic algorithm to encode genes for three typical flight trajectory control points and optimize the population and individuals through gene hybridization and mutation. The optimal flight trajectory for each of the three flight modes is selected by minimizing the cost function. Compared with the traditional equidistant multibaseline flight mode, the imaging results from simulations and measured data show that the proposed algorithm significantly suppresses the grating lobe effect of targets. In addition, oblique UAV flight trajectories are significantly shortened, improving the efficiency of through-the-wall SAR imaging.
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图 1 无人机载穿墙雷达高层建筑探测场景示意图
Figure 1. Schematic diagram of detection scenario for UAV-TWR in high-rise buildings
图 14 实测穿墙场景下不同模式的无人机飞行航迹
Figure 14. Actual measurement of UAV flight trajectory in different modes in through-the-wall scenarios
图 15 理想与实测航迹$ {R_{\rm{pgl}}} $仿真对比
Figure 15. Comparison of $ {R_{\rm{pgl}}} $ between ideal and real trajectory in simulation
图 16 穿墙场景下4种模式的成像情况(“Ⅰ”表示距离-高度向截面，“Ⅱ”表示方位-高度向截面)
Figure 16. Imaging situation of four modes in through-the-wall scenario (“Ⅰ” represents the range-height section, “Ⅱ” represents the azimuth-height section)
表 1 穿墙场景数值仿真参数
Table 1. Simulation parameter settings
参数 数值 雷达载频 2.95 GHz 雷达带宽 440 MHz 场景方位向范围 0～10 m 场景高度向范围 0～5 m 场景距离向范围 0～10 m 墙体厚度 0.2 m 墙体相对介电常数 4.0 点目标1坐标 (8.0 m, 7.0 m, 2.7 m) 点目标2坐标 (5.0 m, 5.0 m, 2.0 m) 点目标3坐标 (2.0 m, 6.0 m, 3.7 m) 超参数权重$ ({w_1},{w_2},{w_3}) $ (0.02, 0.43, 0.55) 
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表 2 算法仿真结果对比
Table 2. Comparison of algorithm simulation results
飞行模式 代价函数 $ {R_{\rm{pgl}}} $(dB) MB-SAR \ −0.51 UMB-SAR −3.15 −8.64 Z-SAR −3.68 −9.73 CF-SAR −4.21 −10.56 OF-SAR −5.25 −11.60
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表 3 实测数据参数
Table 3. Actual measured data parameters
实验参数 数值 雷达载频 2.95 GHz 脉冲重复频率 1923 Hz 雷达带宽 440 MHz 采样率 10 MHz 飞行速度 2 m/s
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表 4 算法实测结果对比(dB)
Table 4. Comparison of algorithm measured results (dB)
算法 $ {R_{\rm{pgl}}} $ MB-SAR −1.91 UMB-SAR −4.14 CF-SAR −7.71 OF-SAR −9.52
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