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This paper presents a computational study and preliminary experimental results of mechanosensor arrays for 2-D airflow sensing. In particular, the analysis presented here applies to a recently developed biomimetic microelectromechanical system (MEMS) inspired by the array of airflow-sensitive hairs found on the crickets' cercus. Based on the maximum-likelihood principle, an estimator that allows reconstruction of both the airflow direction and the airflow velocity amplitude is derived. Given this algorithm, different topologies of hair arrays are investigated in terms of sensitivity for measurement errors. In addition, it is analyzed how 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. Experimental results are shown to validate the theoretical model and its assumptions.