Autonomous Flight within Complex Tactical Terrain to Minimize Noise Based Detection
Small-Unmanned Air Vehicles (sUAV) must navigate communication-denied environments using minimalistic algorithms while avoiding detection. Detection often occurs due to adversaries hearing the noise of the sUAV. The noise is predominantly due to the high speed rotors and engine. We will create and validate a near-real-time autonomous flight path optimization methodology to minimize noise detection of sUAVs in cluttered environments. The approach will be based on advanced analytical methods in aeroacoustics combined with flight trajectory and optimization. This will enable sUAVs to fly while minimizing the chance of noise based detection using psychoacoustic detection models. The approach will allow the warfighter to gather intelligence without detection.