Accurate time delay estimation between signals is crucial for coherent imaging systems such as synthetic aperture sonar (SAS) and synthetic aperture radar (SAR). In such systems, time delay estimates resulting from the cross-correlation of complex signals are commonly used to generate navigation and scene height measurements. In the presence of noise, the time delay estimates can be ambiguous, containing errors corresponding to an integer number of phase wraps. These ambiguities cause navigation and bathymetry errors and reduce the quality of synthetic aperture imagery. In this article, an algorithm is introduced for the detection and correction of phase wrap errors. The random sample consensus (RANSAC) algorithm is used to fit 1-D and 2-D models to the ambiguous time delay estimates made in the time delay estimation step of redundant phase center (RPC) micronavigation. Phase wrap errors are then corrected by recalculating the phase wrap number using the best-fitting model. The approach is demonstrated using the data collected by the 270–330 kHz SAS of the NATO Centre for Maritime Research and Experimentation Minehunting unmanned underwater vehicle for Shallow water Covert Littoral Expeditions. Systems with lower fractional bandwidth were emulated by windowing the bandwidth of the signals to increase the occurrence of phase wrap errors. The time delay estimates were refined using both the RANSAC algorithms using 1-D and 2-D models and the commonly used branch-cuts method. Following qualitative assessment of the smoothness of the full-bandwidth time delay estimates after application of these three methods, the results from the 2-D RANSAC method were chosen as the reference time delay estimates. Comparison with the reference estimates shows that the 1-D and 2-D RANSAC methods outperform the branch-cuts method, with improvements of 29%–125% and 30%–150%, respectively, compared to 16%–134% for the branch-cuts method for this data set.

中文翻译：

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随机样本一致性的相位缠绕纠错与合成孔径声纳微导航的应用

信号之间的准确时间延迟估计对于合成孔径声纳 (SAS) 和合成孔径雷达 (SAR) 等相干成像系统至关重要。在这样的系统中，由复杂信号的互相关产生的时间延迟估计通常用于生成导航和场景高度测量。在存在噪声的情况下，时间延迟估计可能不明确，包含与整数个相位缠绕相对应的误差。这些模糊性会导致导航和测深误差，并降低合成孔径图像的质量。在本文中，介绍了一种检测和校正相位缠绕误差的算法。随机样本一致性 (RANSAC) 算法用于将一维和二维模型拟合到冗余相位中心 (RPC) 微导航的时间延迟估计步骤中做出的模糊时间延迟估计。然后通过使用最佳拟合模型重新计算相位缠绕数来校正相位缠绕误差。该方法使用北约海洋研究和实验中心的 270-330 kHz SAS 收集的数据进行演示，用于浅水隐蔽沿海探险的无人水下猎雷航行器。通过对信号的带宽进行加窗以增加相位缠绕误差的发生来模拟具有较低分数带宽的系统。使用使用一维和二维模型的 RANSAC 算法和常用的分支切割方法来改进时间延迟估计。在对应用这三种方法后全带宽时延估计的平滑度进行定性评估后，选择二维 RANSAC 方法的结果作为参考时延估计。与参考估计值的比较表明，一维和二维 RANSAC 方法优于分支切割方法，分别提高了 29%–125% 和 30%–150%，相比之下，改进了 16%–134%此数据集的分支切割方法。