A Correction Method for Imaging Buried Targets in Layered Media Based on the Range-Doppler Algorithm
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摘要: 合成孔径成像算法一般都是基于均匀介质假设, 在探测海底掩埋目标时, 因声波穿过介质界面时发生折射, 导致成像散焦与定位误差。针对该问题, 文中提出了一种适用于分层介质成像的参数修正算法。首先, 构建了海水-沉积层折射传播模型, 基于斯涅尔定律推导了双程传播时延表达式, 设计了沉积层声速与掩埋深度的联合估计方法; 其次, 将该分层模型嵌入距离-多普勒(R-D)算法, 重新推导并修正了多普勒调频率与距离徙动校正量的解析式; 最后, 通过数值仿真对比了算法修正前后在不同掩埋深度下的成像效果。结果表明, 修正后的算法能有效矫正因折射引起的距离向定位偏差, 改善航迹向聚焦性能, 验证了所提修正算法的有效性。Abstract: Synthetic aperture imaging algorithms are generally based on the assumption of a homogeneous medium. When detecting buried targets on the seafloor, refraction occurs as acoustic waves pass through the medium interface, leading to image defocusing and positioning errors. To address this issue, this paper proposes a parameter correction algorithm suitable for layered medium imaging. First, a seawater-sediment layer refraction propagation model is constructed, and based on Snell's law, an expression for the two-way propagation delay is derived, along with a joint estimation method for sediment layer sound speed and burial depth. Second, this layered model is embedded into the range-Doppler (R-D) algorithm, and the analytical expressions for Doppler frequency modulation and range migration correction are rederived and corrected. Finally, numerical simulations compare the imaging results before and after algorithm correction at different burial depths. The results indicate that the corrected algorithm can effectively rectify range positioning deviations caused by refraction and improve azimuth focusing performance, thereby validating the effectiveness of the proposed correction algorithm.
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Key words:
- synthetic aperture imaging /
- layered media /
- buried targets /
- range-Doppler algorithm
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图 1 LF-SAS探测掩埋目标的声传播模型
Figure 1. Acoustic propagation model for low frequency synthetic aperture sonar detection of buried targets
图 4 不同模型下的距离徙动矫正
Figure 4. Range migration correction under different modelsange cell migration correction under different models
图 5 不同模型下R-D算法成像
Figure 5. Imaging results of the R-D algorithm under different models
图 6 不同模型下R-D算法成像目标切片
Figure 6. Target slice images using the R-D algorithm under different models
表 1 R-D算法在不同介质模型下的成像质量评估
Table 1. Image quality evaluation of R-D algorithm in different media models
掩埋
深度/mPSLR均匀
介质/dBPSLR分层
介质/dBISLR均匀
介质/dBISLR分层
介质/dB1 −15.95 −16.15 −9.64 −9.74 2 −15.65 −16.07 −9.59 −9.70 3 −15.33 −16.04 −9.17 −9.63 4 −14.91 −16.03 −8.83 −9.60 5 −14.20 −15.96 −8.52 −9.48 
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