Performance of DOA estimation algorithms for acoustic localization of indoor flying drones using artificial sound source

Flying unmanned aerial vehicles (UAVs) in swarms can have numerous advantages. However, to maintain a safe distance between them during flight is very challenging. To achieve this, each UAV in the swarms needs to know its relative location with respect to one another. This work proposes a method for relative localization using the chirping sound emitted from UAVs flying together indoors. The strategy is simulated to assess localization performance of three different types of chirping sounds indoors using six microphone arrays. The estimated direction of arrival (DOA) of the chirping sound is calculated using several published algorithms that include MUSIC, CSSM, SRP-PHAT, TOPS and WAVES. The sound is produced in a simulated flying indoor environment with several different settings of sound-to-noise ratio (SNR) and reverberation time (RT). Based on the results, it has been found that chirping sound with a wider frequency band produced better results in terms of mean values of DOA estimation error. The chirping sound performance is also tested with the actual UAVs operating under different rotor speeds. Similarly, it is observed that the chirping sound with wider band also produced better results in three of the algorithms, which is reflected in their absolute mean error. Nevertheless, further work has to be done to filter out the UAVs’ rotor noise and also the indoor reverberation effects for better performance

Cooperative UAV navigation under nominal GPS coverage and in GPS-challenging environments

The project i-TEAMS (innovative TEchniques for Autonomous Micro-UAV Swarms) is aimed at developing new architectures and technologies for distributed guidance, navigation, and control of mini and/or micro Unmanned Aircraft Systems. This paper presents a review of the theoretical and experimental results achieved in both nominal GPS coverage and GPS-challenging environments