Atolla Tech is developing a dual-use (agriculture and defense/security) sensor system and machine learning (ML) algorithm for continuous surveillance and classification of airborne objects. Our solution generates an electronic signature for object identification, analogous to how sonar creates an acoustic fingerprint for submarines. Atolla's system leverages LiDAR technology to detect and identify objects by focusing on their propeller or flapping wings. The ML algorithm applies a waveform analysis by comparing the electronic signature with a database of profiles. It further applies a behavioral analysis of the flight pattern allowing for high-fidelity identification and classification. In defense, Atolla is developing an easy to deploy cUAS detection and identification system that can be set up in FOB, COB and a simplified man portable version for mobile units.