Proc. SPIE. 10648, Automatic Target Recognition XXVIII
KEYWORDS: Signal to noise ratio, Unmanned aerial vehicles, Sensors, Calibration, Digital filtering, Error analysis, Interference (communication), Signal processing, Electronic filtering, Global Positioning System
Spotting a shooter from a drone has been the subject of great interest lately due to its many applications in the fields of defense and security and law enforcement. Using a drone can be an effective way to detect potential threats in many real-life scenarios. Nevertheless, acoustic signals recorded from a drone usually exhibit a very low SNR, mainly due to the distance to the source and the proximity of the sensors to the propellers. This is a serious limiting factor and, therefore, the use of signal enhancement techniques is required. This work addresses the problem of determining the Direction-of-Arrival (DoA) of the muzzle blast, captured using a planar microphone array mounted on a commercial DJI PHANTOM 4 drone in flight. This new shooter localization method that relies solely on detecting and estimating the DoA of the muzzle blast. However, the typical low SNR in this scenario requires the use of preprocessing techniques, such as signal clipping and median filtering, to enhance the signal of interest (muzzle blast). In addition, we employ a recently introduced improved data selection DoA estimation method suitable for gunshot signals recorded from a low to medium altitude mobile aerial platform. Positive results achieved indicate that this approach is effective and of practical interest.