Acoustic UAV detection method based on blind source separation framework

Abstract

Many new challenges are encountered in public security because of the rapid development of an unmanned aerial vehicle (UAV) technology. In order to meet these challenges brought by UAV, it is necessary to realize timely and accurate UAV detection. In this paper, an acoustic UAV detection method based on blind source separation (BSS) framework is introduced to solve the UAV sound detection problem with multi-source interference. In the framework proposed in this paper, source number estimation is used to determine the type of BSS problem, and three methods are applied to separate the UAV sound from the mixing signal in different BSS situations. In the experiment, a variety of machine learning algorithms are used to detect UAVs. The robustness of the proposed method is tested on different UAVs and different sound features. Experimental results show that the UAV sound detection method based on the BSS framework realizes the effective detection of UAVs in different tests and achieves excellent robustness. Compared with mixed signal and filtered signal, the detection rate of our scheme is more than 90%, whether in the overdetermined, positive-definite, and underdetermined cases.