Sidelobes in swath sonar water column imagery can obscure targets of interest and create erroneous bottom detections. Differences between ideal and actual element responses limit the achievable sidelobe level for practical arrays. Therefore, removing or reducing the errors by calibration may reduce the sidelobe level. We examine an autocalibration technique for swath sonars that use data collected while mapping. Our method is based on the generalized interferometric array response (GIAR). The GIAR values are used to find pointlike signals and maximized to estimate the amplitude and phase errors for each element. On simulated data, this reduces the sidelobe level to below $-$ 50 dB. In the field, we find the calibration estimate stable over ping groups and that most sidelobes improved by 2 dB or more. This suggest that GIAR calibration can work well when the calibration matches the swath sonar system. However, some sidelobes are also unchanged or degraded by a few decibels. An extended calibration model seems necessary to achieve further improvements in the field.

中文翻译：

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Swath 声纳的数据驱动自动校准

条带声纳水柱图像中的旁瓣可能会掩盖感兴趣的目标并产生错误的底部检测。理想和实际元件响应之间的差异限制了实际阵列可实现的旁瓣电平。因此，通过校准消除或减少误差可能会降低旁瓣电平。我们研究了用于使用测绘时收集的数据的条带声纳的自动校准技术。我们的方法基于广义干涉阵列响应 (GIAR)。GIAR 值用于查找点状信号并最大化以估计每个元素的幅度和相位误差。在模拟数据上，这将旁瓣电平降低到以下$-$ 50 分贝。在现场，我们发现校准估计在 ping 组上是稳定的，并且大多数旁瓣改善了 2 dB 或更多。这表明当校准与条带声纳系统匹配时，GIAR 校准可以很好地工作。然而，一些旁瓣也没有改变或降低了几分贝。扩展校准模型似乎有必要在该领域实现进一步改进。