  Arrays of bioinspired artificial hair-cell airflow velocity sensors can
enable flight-by-feel of small, unmanned aircraft. Natural fliers - bats,
insects, and birds - have hundred or thousands of velocity sensors distributed
across their wings. Aircraft designers do not have this luxury due to size,
weight, and power constraints. The challenge is to identify the best locations
for a small set of sensors to extract relevant information from the flow field
for the prediction of flight control parameters. In this paper, we introduce
the data-reducing Sparse Sensor Placement Optimization for Prediction algorithm
which locates near-optimal sensor placement on airfoils and wings. For two or
more sensors this algorithm finds a set of sensor locations (design point)
which predicts angle of attack to within 0.10 degrees and ranks within the top
1% of all possible design points found by brute force search. We demonstrate
this algorithm on several variations of airfoil sections of infinite and finite
wings in clean and noisy data, evaluate model sensitivities, and show that the
algorithm can be used to identify an appropriate number of sensors for a given
accuracy requirement. Applications for this algorithm are explored for aircraft
design and flight-by-feel control.
