A wavelet based data processing algorithm providing automation of the process of time–frequency analysis of transients obtained by statistical UAV motion modeling for a large number of randomly realized sets of tolerances has been developed. Statistical UAV motion modeling is the main tool for analyzing and checking out the functioning of the developed control algorithms according to possible scatter of UAV characteristics and environmental parameters. Based on the quality of the transients obtained in the statistical modeling, acceptability of the selected control algorithm parameters in terms of ensuring a stable UAV flight in a given range of permissible trajectories according to the tolerances is determined. The greatest difficulty in this case is automation of the time-frequency analysis of the obtained transients, since standard diagnoses do not allow identifying critical variants of the tolerance combinations that have spectral components in the transients relevant to the studied parts of the trajectory exceeding the specified values of frequency, amplitude and duration. To solve this problem, the developed algorithm uses wavelet and wavelet–packet transforms of one-dimensional signals, which are precisely the type of time– frequency transforms of the signals, in order to obtain and then analyze the time–frequency representations of the transients with specified parameters. An example of using the developed algorithm to estimate the parameters of spectral components, such as duration, maximum and average amplitude value, frequency in the neighborhood of the point with the maximum amplitude value, required to determine the quality of the transients obtained by the modeling is given.
Точилова О.Л., Колготин А.В. Алгоритм автоматизации частотно–временного анализа переходных процессов, полученных при моделировании движения БПЛА. Математическое моделирование и численные методы, 2021, № 1, с. 49–65.