Analysis of acoustic direction finding methods for unmanned aerial vehicles

Abstract

Currently, classical means of detecting objects do not provide the necessary efficiency for detecting small UAVs, and acoustic location among the known methods for their observation is the most cost-effective solution.
 The article analyzes the well-known methods of direction finding of acoustic signals in order to select algorithms for processing UAV signals. Obtaining qualitative indicators of the analyzed algorithms was carried out by the method of statistical computer modeling in the Matlab environment.
 Based on the simulation results, it is shown that classical methods are the most stable under conditions of low signal-to-noise ratios. The GCC-PHAT direction finding algorithm, based on determining the difference in the time of arrival of a signal at spaced points, is computationally economical and simple enough to determine the direction to the UAV, but it is not capable of distinguishing more than one radiation source within the diagram orientation. Beamforming methods are also relatively easy to implement and computationally efficient, and are more robust at low signal-to-noise ratios. The SRP-NAM algorithm has a greater accuracy in determining angles than SRP-PHAT, so it can be an adequate replacement for the SRP-PHAT algorithm.
 High-resolution methods provide better directional resolution than classical methods, which, in the case of a limited microphone array aperture, is a positive factor in the design of an UAV direction finding station. High resolution methods were considered: non-coherent MUSIC, non-coherent normalized MUSIC and TOPS method. It is shown that incoherent MUSIC gives poor results in distinguishing close UAV signals, since unequal estimates of the entire frequency range are concentrated during bearing formation. The incoherent normalized MUSIC algorithm is able to efficiently use the entire frequency range of the UAV acoustic signal. The TOPS algorithm is inferior to the incoherent normalized MUSIC algorithm, and on the other hand, it does not require a priori estimates of the number of radiation sources.