Data parser approaches for (online) parameter estimation

Finding maneuvers for parameter estimation in flight data records is a laborious task and traditionally performed post-flight on ground. Two different data parser approaches to automatically detect these maneuvers in flight are presented. Both methods search the control input signals for significant changes that correspond to test maneuvers. The first algorithm is based on the signal time derivative of the input signal whereas the second method uses a fast orthogonal wavelet transform. Both algorithms are tested with flight test data recorded with the DLR research aircraft ATTAS. Performance results are compared and potential problems when applying the parsers to other data are discussed. Results indicate that both methods are applicable in an online parameter estimation tool. The intention of the work in this paper is to develop an algorithm with a high level of automation for in-flight use, but both approaches could also be applied to offline flight data mining problems. © Deutsches Zentrum für Luft- und Raumfahrt e.V. 2014.