This work focuses on the development of a distributed network of compact microphone arrays for unmanned aerial vehicle (UAV) detection and tracking. Each compact microphone array extends in a 10 cm length aperture and consists in an arrangement of digital MEMS microphones. Several arrays are connected to a computing substation using the I2S, ADAT and MADI protocols using optical fiber. These protocols used together allow to collect the signals from hundreds of microphones spread over a distance of up to 10 km. Sound source localization is performed on each array using measured pressure and particle velocities. The pressure is estimated by averaging the signals of multiple microphones, and the particle velocity is estimated with high order finite differences of microphone signals. Multiple calibration procedures are compared experimentally. Results in sound source localization, noise reduction by spatial filtering and UAV recognition using machine learning are presented.