Achieving autonomous in complex natural environments has the potential to transform society by bringing the benefits of automation from the confines of the factory floor to the outdoors. There, it could benefit areas such as environmental monitoring and clean-up, precision agriculture, delivery of goods. A fundamental requirement for achieving these goals are sensors that can provide reliable support for navigation, e.g., a drone, in natural environments. In this thesis, sonar-based navigation has been investigated as an approach to parsimonious autonomous sensing for drones. Bats living in dense vegetation have demonstrated that autonomous navigation in a complex, natural environment based on two one-dimensional ultrasonic echo streams is feasible. Here, a biomimetic sonar head has been used to collect echo data from recreations of natural foliage in the lab under controlled conditions. This data was used to address the research question whether the grazing angle at which the sonar is looking at a surface can be estimated from the echoes -- despite the random three-dimensional nature of the scatter from the foliage. To investigate this, the echoes have been subjected to statistical analysis such as spectral coherence and cross-correlation. Most importantly, the foliage data was compared against predictions made by the Endura method (energy, duration, and range method) that has been devices for two-dimension random scatterers. The results of this analysis shows that -- despite their profoundly random nature -- echoes can be used to estimate the sonar grazing angle directly, i.e., without the need to resort to reconstructions of the foliage geometry. This opens the possibility of developing simple devices for navigation control in natural environments that can control the direction of motion at a very little computational cost.