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.

@en

This work aims to determine the optimal microphone placement on an acoustic array of TU Delft’s ‘V-tunnel’ which is used for beamforming in aero-acoustic studies. The beamforming performance is driven by two parameters; the Maximum Side lobe Level (MSL) and the Main Lobe Width (MLW). The array design should give a good trade-off between these parameters. The proposed optimization method has two optimization loops. First, the main loop consists of design variables used to collectively describe the distribution of microphones. Then the nested loop generates arrays which satisfy the geometry descriptions from the main loop. Finally, the main loop searches for the optimal design variables. The optimized array is able to achieve the MSL below -15 dB up to the distance approximately four times the MLW around the main lobe. Experimental validation was also carried out to compare the optimized array’s performance with a benchmark array and an array from the beginning of the optimization.