A computational study of biomimetic mechanosensor array processing for air flow sensing

We present a computational study of mechanosensor arrays for air flow sensing. The analysis presented here applies to air flow sensors consisting of arrays of hairs e.g., both the array of air flow sensitive hairs found on the crickets' cercus as well as a recently developed biomimetic MEMS system. Based on the maximum likelihood principle we derive an estimator which allows reconstruction of both the air flow direction in 2D and the air flow velocity amplitude assuming the air flow to be the same at all the hairs in the array. Given this algorithm, different topologies of hair arrays are investigated in terms of sensitivity for measurement errors. Also, we analyze how the redundancy i.e, the presence of many more than the minimum number of hairs, in the array can be used for lessening the effect of measurement noise. Multiple types of hardware failure common in biological and artificial array systems as well as the occurrence of saturation are analyzed and it is shown how redundancy again increases the robustness of the sensor.