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Forward-looking Imaging via Iterative Super-resolution Estimation in Airborne Multi-channel Radar
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摘要: 波达角估计算法用于机载多通道雷达前视成像时可以突破瑞利极限，实现同一波束主瓣宽度内的多目标分辨，改善成像的方位向分辨率，然而天线波束覆盖有限且其快速扫描使得可用于协方差矩阵估计的数据样本缺乏，导致对目标位置和幅度估计出现误差。该文提出了一种基于单快拍迭代超分辨处理的多通道雷达前视成像算法，通过对单个空域快拍的迭代谱估计可获得目标的准确位置和幅度信息，再通过多个脉冲的非相干累积得到前视方位高分辨成像。仿真和实测数据处理结果表明，所提算法具有分辨多目标的能力，相较于传统前视成像算法显著提高了前视图像的方位分辨率，同时保证了点目标的精确重构和面目标的轮廓重构。Abstract: The direction of arrival estimation algorithm can overcome the Rayleigh limit, effectively separate multiple targets within the main lobe, and improve the azimuth resolution when applied to forward-looking imaging in airborne multi-channel radar. However, the limited antenna beam coverage and rapid scanning result in a scarcity of data samples available for covariance matrix estimation, leading to direction and amplitude estimation errors. Herein, we propose a forward-looking imaging algorithm based on single-snapshot iterative super-resolution estimation. The algorithm performs single-snapshot iterative spectral estimation to accurately determine the azimuth and amplitude of the target. Subsequently, a high-resolution image is achieved through non-coherent accumulation. Simulation and experimental data processing results show that the proposed algorithm can resolve multiple targets, significantly improving the azimuth resolution of the forward-looking image compared with traditional forward-looking imaging algorithms. Moreover, it ensures the accurate reconstruction of point targets and contour reconstruction of area targets.
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图 1 机载多通道雷达前视成像三维几何结构图
Figure 1. 3D geometry for airborne multi-channel radar forward-looking imaging
图 3 迭代超分辨多通道雷达前视成像处理流程图
Figure 3. Flow chart of iterative super-resolution forward-looking imaging processing of multi-channel radar
图 12 海面区域实测数据成像结果
Figure 12. Forward-looking imaging results for the measured data of sea surface
1 迭代超分辨算法流程
1. Flow chart of iterative super-resolution algorithm
根据当前波束中心 $ \bar \theta $，由式(12)计算得空域不混叠范围；在此范
围上构建空域导引矢量矩阵A初始化： ${\hat {\boldsymbol{x}}_1} = {{\boldsymbol{A}}^{\text{H} } }{\boldsymbol{s}}$，基于此计算信号的空间功率分布矩阵 ${{\boldsymbol{P}}_1}$ 重复以下操作： ${{\boldsymbol{w}}_{t + 1} } = {\left( {{\boldsymbol{A}}{{\boldsymbol{P}}_t}{{\boldsymbol{A}}^{\text{H} } } + {{\boldsymbol{R}}_{\rm{N}}} } \right)^{ - 1} }{\boldsymbol{A}}{{\boldsymbol{P}}_t}$ ${\hat {\boldsymbol{x} }_{t + 1} } = {\boldsymbol{w} }_{t + 1} ^{\text{H} }{\boldsymbol{s} }$ ${\hat {\boldsymbol{P}}_{t + 1} } = [{\hat {\boldsymbol{x}}_t}\hat {\boldsymbol{x}}_t^{\text{H} }] \odot {{\boldsymbol{I}}_{K \times K} }$ 直到收敛 
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表 1 点目标仿真实验系统参数
Table 1. Point target simulation parameters
参数名称 参数值 雷达波长(m) 0.03 系统带宽(MHz) 20 采样频率(MHz) 30 脉冲重复频率(Hz) 1000 通道数 8 通道间隔(m) 0.09 波束方位向主瓣宽度(°) 2.11 机载平台运动速度(m/s) 100 波束扫描速度(°/s) 100 前视扫描范围(°) –10～10 目标点距离(m) 1000, 1250, 1250, 1250, 1500 目标点方位角(°) 0, –3, –2, 3, 0 目标点SNR (dB)
20, 25, 25, 20, 20
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表 2 场景仿真实验系统参数
Table 2. Parameters of simulation for simulated scenario
参数名称 参数值
(场景1)参数值
(场景2)雷达波长(m) 0.03 0.03 系统带宽(MHz) 40 40 采样频率(MHz) 50 50 脉冲重复频率(Hz) 1000 1000 通道数 8 8 通道间隔(m) 0.045 0.120 波束方位向主瓣宽度(°) 4.23 1.59 波束扫描范围(°) –20～20 –8～8 机载平台运动速度(m/s) 100 80
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表 3 实测数据系统参数
Table 3. Parameters of measured data
参数名称 第1组实测数据 第2组实测数据 雷达波段 X Ku 脉冲重复频率(Hz) 417 3125 通道数 4 4 通道间隔(m) 0.150 0.037 波束主瓣宽度(°) 2.42 6.31 波束扫描范围(°) –14～14 –20～20 扫描速度(°/s) 80 70 机载平台速度(m/s) 145 68
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表 4 地面实测数据成像结果图像熵和对比度对比
Table 4. Entropy and contrast of the measured data results
成像方法 图像熵 对比度 实波束成像 3.8625 4.42 单脉冲成像结果 1.6530 8.12 迭代超分辨成像 1.2543 9.59
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