What information do Karman streets offer to flow sensing?

In this work, we focus on biomimetic lateral line sensing in Karman vortex streets. After generating a Karman street in a controlled environment, we examine the hydrodynamic images obtained with digital particle image velocimetry (DPIV). On the grounds that positioning in the flow and interaction with the vortices govern bio-inspired underwater locomotion, we inspect the fluid in the swimming robot frame of reference. We spatially subsample the flow field obtained using DPIV to emulate the local flow around the body. In particular, we look at various sensor configurations in order to reliably identify the vortex shedding frequency, wake wavelength and downstream flow speed. Moreover, we propose methods that differentiate between being in and out of the Karman street with > 70% accuracy, distinguish right from left with respect to Karman vortex street centreline (> 80%) and highlight when the sensor system enters the vortex formation zone (> 75%). Finally, we present a method that estimates the relative position of a sensor array with respect to the vortex formation point within 15% error margin