Dedrone, the market leader in smart airspace security, announced the launch of the newest version of DedroneTracker.AI, Version 6.0. The updates, based on feedback from law enforcement officials, security professionals and military operators in the field, represent a major leap forward in AI/ML enabled autonomous threat detection and classification capabilities demanded by our most strategic customers. DedroneTracker.AI continues to offer best-in-class sensor-fusion, AI/ML enabled threat risk prioritization, autonomous threat interrogation and classification for drones. An early rollout of this version was first adopted by the US federal government as well as a number of NATO member governments and is now available for all Dedrone clients as a software update.  

DedroneTracker.AI Version 6.0 also delivers a comprehensive list of US government CsUAS pre-configured integrations, enabling any government customer to rapidly field a “System-Of-Systems" capability with DedroneTracker.AI as the Single Pane of Glass for complex CsUAS systems. Version 6.0 also includes bi-directional integration between the DedroneTracker.AI platform and the Aerial Armor software platform, representing the first step in complete integration between Dedrone and the recently purchased Aerial Armor. In advance of Federal Aviation Administration (FAA) regulations requiring the use of Remote ID enabled drones or broadcast modules becoming effective in September 2023, the new version of DedroneTracker.AI supports the latest US, EU, and Japanese Remote ID standards, and offers improved integration and consolidation of Remote ID detections with non-Remote ID detections.

“With this latest version of our AI platform, we have significantly improved end-to-end autonomy for CUAS and enable safe, productive drone usage,” said Aaditya Devarakonda, CEO of Dedrone. “This leap in capabilities comes from proprietary motion and computer vision models to classify any and all flying objects in a 3D airspace. Additionally, we have data from millions of drone flights and pictures/videos of UAS on different backgrounds constantly training and enhancing these neural networks. Today, our massive sensor network continues to enhance these learning models to the next level creating a moat that truly differentiates our solution versus both legacy and emerging competitors.”

By taking inputs from multiple sensors including radio frequency (RF), radar, video and acoustics, it confirms drone presence and determines precise location of drone and pilot. Based on behavior, imagery, historical flight data, and other inputs, the AI engine offers the operator a single queue of risk- prioritized targets through autonomous background interrogation of unauthorized drones while simultaneously tracking multiple friendly drones. The new version of the software also allows for the operation of mitigation tools directly from the same interface.