3-D Localization of UAV and Detection based on Harmonics Index and Spectral Entropy Criteria

Abstract

The popularity of drones or unmanned aerial vehicles (UAVs) will be increased enormously in future smart cities due to their usage in several domains. Significantly it’s an emerging research to localize and timely detect such kinds of objects to avoid threats. This paper considered to deploy an array of 4-microphones for acoustics localization enhancement and UAV detection optimization. For high accuracy of acoustics localization in 3-D, the cumulative algorithm of GCC-PHAT and least square (LS) based on TDOA is implemented to estimate acoustic direction of arrivals (DOAs). However, detection optimization is achieved by a spectral framework, where harmonics index estimation effectively determined sound and noise range in each frame, while spectral entropy featured noise and sound correlation, the least possible value of entropy verified the presence of UAV and optimize its detection. Moreover, experimental results confirmed the efficiency of proposed techniques, our system has robust performance for localization as it featured less than 5% errors, while detection accuracy is more than 75%.