发表机构
University of Antwerp; Flanders Make Strategic Research Centre（安特卫普大学; 法兰德斯制造战略研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种仅利用声纳数据和机器人里程计的自监督去卷积方法，学习点扩散函数模型，在仿真中恢复发射器方向性，实现全视场5-6度恒定角分辨率，优于经典去卷积和CLEAN算法。
AI 中文摘要
具有稀疏麦克风阵列的宽带在空气声纳传感器通过延迟求和波束形成来形成图像，这导致高旁瓣和向视场两侧严重退化的角分辨率。对这些图像进行去卷积需要访问完整成像链的点扩散函数，包括发射器频谱和方向性以及阵列缺陷。在实践中，这些方面很难校准。在本信中，我们提出了一种自监督方法，该方法学习一个去卷积网络以及点扩散函数的物理结构模型，仅需要声纳数据本身和安装传感器的移动机器人的里程计。利用声学流扭曲，模型的内部表示被扭曲，随后通过学习的点扩散函数进行渲染。然后，根据图像形成的统计模型，在散斑似然下将其与测量图像进行比较。在仿真中，学习模型恢复了未知的发射器方向性，去卷积图像在整个视场上达到5至6度的恒定角分辨率，而延迟求和为12至30度，带相干因子的延迟乘加和为7至23度。该方法优于使用标称点扩散函数的经典去卷积，甚至优于使用真实点扩散函数的CLEAN算法。
英文摘要
Broadband in-air sonar sensors with a sparse microphone array form images through delay-and-sum beamforming, which leaves high sidelobes and an angular resolution that degrades strongly towards the sides of the field of view. Deconvolving these images requires access to the point-spread function of the complete imaging chain, including the emitter spectrum and directivity and the array imperfections. In practice, these aspects are hard to calibrate for. In this letter, we propose a self-supervised method that learns a deconvolution network together with a physics-structured model of the point-spread function, requiring only the sonar data itself and the odometry of a moving robot on which the sensor is mounted. Using acoustic flow warping, the internal representation of the model is warped and subsequently rendered through the learned point-spread function. This is then compared to the measured images under a speckle likelihood in accordance with the statistical model of image formation. In simulation, the learned model recovers the unknown emitter directivity, and the deconvolved images reach a constant angular resolution of 5 to 6 degrees over the full field of view, against 12 to 30 degrees for delay-and-sum and 7 to 23 degrees for delay-multiply-and-sum with coherence factor. The method outperforms classical deconvolution with the nominal point-spread function, and even outperforms CLEAN with the true one.
