Acoustic Localization System for Precise Drone Landing

We present MICNEST: an acoustic localization system enabling precise drone landing. In MICNEST, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics; iii) as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show that MICNEST can localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing, MICNEST can achieve a success rate of 94 %. The average landing error (distance between landing point and target point) is only 4.3 cm.