Unconstrained Synthetic Aperture Sonar

声纳是水下应用中必不可少的传感器，因为它可以在较差甚至无能见度的条件下和较长的距离上提供数据。但是它的空间分辨率依赖于传感器的组合，通过干涉（大致）近似采样光束。在更大的区域上放置更多的换能器相应地提供更高的分辨率。但是，声纳传感器中换能器的数量受到传感器尺寸、功耗和成本等诸多因素的限制。因此，一种流行的方法是使用合成孔径，即将声纳的N个换能器放置在k个位置，以产生具有kN换能器的虚拟传感器。这种合成孔径声呐（SAS）的技术水平与使用方式的限制密切相关。例如，k个姿势通常必须等距地放置在垂直于传感器的直线上。这样做的动机是为了简化信号处理，同时也考虑到实际情况：水面船只或自主水下航行器（AUV），其声纳面朝海底，只需要在直线上以恒定速度航行。但这也极大地限制了飞行器的范围。该项目为无约束SAS奠定了基础，即a)可以在任意轨迹上计算b)不需要导航传感器数据（GPS， INS等）的SAS。如初步工作所示，原始扫描的注册提供了足够精确的位置估计，以计算场景的高分辨率空间重建。关于a)对无约束SAS问题的公式和解的贡献得到了推导。这些也是相关领域感兴趣的，例如，在机器人或汽车等移动系统上使用雷达，雷达遥感或超声波医学成像。关于b)，研究了稳健的2.5D和3D配准方法，特别是光谱方法，它非常适合受噪声强烈影响的原始数据。

Sonar is an essential sensor for underwater applications as it provides data under bad or even no visibility conditions and over longer distances. But its spatial resolution depends on a combination of transducers to (roughly) approximate sampling beams by interferences. A larger number of transducers placed on a larger area accordingly provide a higher resolution. But the number of transducers in a sonar sensor is limited by many factors like sensor size, power consumption and costs. A popular approach is hence the use of a synthetic aperture, i.e., the sonar with its N transducers is positioned at k places to generate a virtual sensor with kN transducers.The state of the art for this synthetic aperture sonar (SAS) is strongly coupled to constraints on the way it can be used. For example, the k poses often have to be equidistantly placed on a line perpendicular to the sensor. This is motivated by the intention to ease the signal processing as well as by practical aspects: a vehicle, e.g., a surface vessel or an Autonomous Underwater Vehicle (AUV), with a sonar facing down to the sea-floor is only required to navigate with constant speed on a straight line. But it also significantly limits the scope of the vehicle.This project develops the foundations for an unconstrained SAS, i.e., a SAS that a) can be computed on arbitrary trajectories b) without the requirement of navigation sensor data (GPS, INS, etc.). As shown in preliminary work, a registration of the raw scans provides sufficiently precise location estimates to compute a higher-resolution spatial reconstruction of the scene. With respect to a) contributions to the formulation and solution to the unconstrained SAS problem are derived. These are also of interest to related areas, e.g., the use of radar on mobile systems like robots or automobiles, remote sensing with radar, or medical imaging with ultra-sound. With respect to b) methods for robust 2.5D and 3D registration are investigated, especially spectral methods, which are very well suited for raw data that is strongly affected by noise.