Comparison of acoustic localisation techniques for drone position estimation using real-world experimental data

Due to technological advances in the drone industry, security threats induced by unmanned aerial vehicles (UAVs) are becoming more relevant. Fast and accurate localisation systems need to be designed. One approach is localisation of UAVs by their sound using acoustic techniques. So far, a systematic performance assessment of acoustic techniques for drone localisation, based on real-world data, is lacking. This work presents a comparison of selected techniques using real-world measurement data. The achieved performance serves as a baseline for future design of novel localisation methods. Three techniques are chosen. The first technique estimates the time-difference-of-arrival (TDOA) using generalised cross-correlation with phase transform weighting (GCC-PHAT). The second technique is differential evolution, which approaches the localisation task as a global optimisation problem. The third technique is conventional frequency domain beamforming. Real-world data of 5 quadrotor UAVs were used acquired with an acoustic microphone-array. The performance of the techniques is assessed using the absolute error between the estimated source location and the true source location obtained from the onboard GPS tracker of the drones. GCC-PHAT and differential evolution attempt to estimate the drone position in one or few steps. They have a much shorter runtime than beamforming, which is an exhaustive grid search algorithm. However, these techniques result in lower detection ranges and accuracy compared to beamforming.