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This paper addresses the problem of identifying different flow environmentsfrom sparse data collected by wing strain sensors. Insects regularly performthis feat using a sparse ensemble of noisy strain sensors on their wing. First,we obtain strain data from numerical simulation of a Manduca sexta hawkmothwing undergoing different flow environments. Our data-driven method learnslow-dimensional strain features originating from different aerodynamicenvironments using proper orthogonal decomposition (POD) modes in the frequencydomain, and leverages sparse approximation to classify a set of strainfrequency signatures using a dictionary of POD modes. This bio-inspired machinelearning architecture for dictionary learning and sparse classification permitsfewer costly physical strain sensors while being simultaneously robust tosensor noise. A measurement selection algorithm identifies frequencies thatbest discriminate the different aerodynamic environments in low-rank PODfeature space. In this manner, sparse and noisy wing strain data can beexploited to robustly identify different aerodynamic environments encounteredin flight, providing insight into the stereotyped placement of neurons that actas strain sensors on a Manduca sexta hawkmoth wing.

Environment Identification in Flight using Sparse Approximation of Wing Strain