Near-range Strong Coupled Signal Suppression Algorithm Based on RPCA for GBSAR
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摘要:
地基合成孔径雷达(GBSAR)是一种全天时全天候非接触式大面积区域高精度形变监测手段，在矿区、边坡、大坝等区域的监测具有广泛应用。在封闭空间监测站中对外场进行连续监测时，雷达接收的回波信号会受到封闭空间的强散射信号干扰。近距离强散射信号耦合到雷达接收端形成虚假目标，严重影响成像质量。该文提出使用RPCA算法，在距离多普勒域将回波信号分解为低秩和稀疏两部分，利用距离多普勒域耦合信号的低秩特性，以及场景信号的稀疏特性，将耦合信号与场景信号有效分离。不同于基于PCA的已有耦合信号抑制方法，RPCA对场景回波信号本身没有高斯分布假设要求，这一假设要求在实际中通常是不满足的。此外，该文提出基于相关性分析的RPCA正则化系数优化选择方法，以实现低秩与稀疏的较优分离。该文通过实际GBSAR数据处理验证了方法的有效性，相比于已有的基于PCA的算法，基于RPCA的耦合信号抑制方法能够在保留场景回波信号的同时更好地抑制耦合信号。
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关键词:
- 地基合成孔径雷达(GBSAR) /
- 耦合信号抑制 /
- 鲁棒主成分分析(RPCA) /
- 主成分分析(PCA)
Abstract:Ground-Based Synthetic Aperture Radar (GBSAR) is an all-day all-weather, non-contact, high-precision instrument for wide-area deformation monitoring, which has been widely used to monitormining areas, slops, and dams. When monitoring the outside scene with the radar placed in the inner space, the radar echo would be interfered with by strong scattering signals reflected from the inner space. The strong scattering signal at near range would severely affect the image quality. Therefore, this paper proposes a Robust Principal Component Analysis(RPCA) based algorithm to decompose the range-doppler domain signal into low-rank and sparse parts,as, in the range-doppler domain, the near-range coupled signal has low-rank characteristics, whereas the scene signal has sparse characteristics. Unlike the existing Principal Component Analysis(PCA) based algorithm, the proposed RPCA algorithm does not assume a Gaussian-distributed scene signal, which usually could not be satisfied in reality. Additionally, this paper proposes a correlation-based regularization parameter optimization method for RPCA. Thus, low rank and sparse matrices can be better separated. Furthermore, the proposed method is verified with real GBSAR data. The result shows that the proposed RPCA based method can better suppress the coupled signal while retaining the scene signal than the existing PCA-based algorithm.
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表 2 图像熵指标参数
方法 –30°斜视角图像熵 0°斜视角图像熵 30°斜视角图像熵 原始图像 7.2602 7.2139 7.2850 基于PCA方法 7.2303 7.0497 7.3193 本文RPCA方法(常系数c：0.7, 0.7, 0.7) 7.2349 7.0521 7.2662 本文RPCA方法(最优常系数c：0.83, 0.82, 0.83) 7.1759 7.0085 7.2148 本文RPCA方法(常系数c：0.9, 0.9, 0.9) 7.2227 7.0417 7.2594 
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