Аnalysis of frequency-time structure of acoustic noise of small automatic air systems
Keywords:automatic air systems, acoustic noise, correlation analysis, signal model, classifying feature.
The statement of the problem of detecting small automatic air systems (drones) is formulated, the expediency of building a drone detection system on the principle of receiving and analyzing acoustic signals emitted by drones during their flight mission is substantiated.
The study of temporal fluctuations of the period of the acoustic signals of the drone is carried out by the method of model-correlation analysis, as a result of which three-dimensional structures are formed: time – period – the correlation coefficient of the acoustic signal with the model in the form of a sinusoidal function limited in time.
The resulting structures are formed as matrices of correlation coefficient values.
The members located along the columns are calculated with the time shift of the model function along the signal sample. The members in each column are calculated for a constant period of the model function set from a number of values.It is shown that the correlation coefficients between the matrix rows calculated from the drone signals are significantly higher than the same values obtained from the background noise measurements. The functions showing the change in time of the correlation coefficients between the rows of the matrices of the time – period structures for drone signals and background noise do not intersect and show a consistently large difference in the correlation coefficients, which allows the correlation coefficient to be used as a classifying feature when recognizing drone signals.
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