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Atolla Tech

SWAP optimized C-UAS detection and identification system
Austin

About Atolla Tech

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.

Team

Problem statement

The rapid advancements in unmanned aerial systems (UAS) technology have led to an increasing defense and security threat. Both state and non-state actors are leveraging commercially available systems, capitalizing on the miniaturization and autonomy of these devices. In defense applications, expensive standalone fixed-wing systems can be complemented by modified quadrotor and fixed-wing UAS assembled from commercial components. This expands the use of drones to squad-level operations and special forces missions in artillery targeting, anti-armor, and anti-ship capacities. More sophisticated adversaries may employ swarms of diverse UAS types to conduct complex operations. Such swarms can overwhelm surface-to-air missile (SAM) defenses, exposing these systems to targeted follow-up attacks. Swarms also enable intra-drone communication, reducing the impact of jamming and allowing for multi-vehicle kill systems.
The miniaturization and affordability of UAS components also empower bad actors in urban environments. UAS can be employed for espionage with a low risk of detection, drug trafficking across borders, or even hacking via drone-delivered equipment. Terrorist groups have also utilized retrofitted civilian drones. The use of commercial UAS allows malicious actors to disguise their intent, complicating counter-UAS (C-UAS) efforts. The inability to differentiate between threat and non-threat is exacerbated by the current high cost of interception.

Traction information

Selected for NSIN Propel Boston cohort (5% acceptance ratio). Previously selected for AF MassChallenge and AF STTR Phase I.

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