Counter-UAS Drone Defense: Capturing Drones with LiDAR, Nets, and Parachutes
UAV News Talk – Drone News, eVTOLs, UAS Policy & Air MobilityShow Notes
Max Trescott talks with David Hall, founder and CEO of Velodyne Space, about a new approach to counter-UAS defense that focuses on capturing drones using LiDAR, nets, and parachutes instead of destroying them. Hall begins by explaining LiDAR—Light Detection and Ranging—and how it measures distance by timing how long it takes for pulses of light to travel out and return. While LiDAR has existed for decades, Hall describes how his work during the DARPA Grand Challenge helped transform LiDAR from slow, single-beam mapping tools into spinning, multi-beam systems capable of real-time 3D perception. Those systems made it possible for autonomous vehicles to reliably understand their surroundings and plan motion in real time. Initially, Hall experimented with camera-based vision systems for self-driving vehicles, but he found they were easily confused by reflections, shadows, and visual artifacts. Writing software to compensate for every failure mode quickly became impractical. LiDAR offered a fundamentally different solution by providing direct distance measurements rather than inferred depth. By stacking dozens of laser beams vertically and rotating the entire sensor to achieve 360-degree coverage, Hall created a top-down, real-time view of the environment that proved autonomy was achievable. That experience—combining sensing, computation, and mechanical systems—eventually led Hall into the world of counter-UAS. Roughly a decade ago, he began exploring electromagnetic launch technology as an alternative to rockets for space launch. While studying high-power coil-based systems, Hall realized the same technology could be applied to a much more immediate problem: stopping drones. As drones have become more capable and more accessible, they’ve also become harder to defeat. Hall explains that many counter-UAS systems rely on electronic warfare techniques such as RF jamming, GNSS interference, or cyber takeover. These approaches can fail against autonomous drones or drones controlled via fiber-optic cables, which are increasingly common in conflict zones. Kinetic approaches, on the other hand, risk sending heavy debris falling into populated areas. Hall’s solution is a capture-based counter-UAS system. Instead of disabling a drone and letting it fall, the system fires a net that physically entangles the aircraft. Once captured, a parachute deploys, slowing the descent so the drone can fall safely even in populated environments. This makes the system particularly attractive for airports, cities, and other civilian infrastructure, where safety and liability are critical concerns. The system relies on a layered sensing approach. Cameras combined with AI identify drones and distinguish them from birds, while LiDAR provides precise range information needed to time the net deployment. Hall explains that LiDAR doesn’t need millimeter-level precision in this application; knowing distance within a few feet is sufficient to ensure the net opens at exactly the right moment. This combination allows the system to engage drones at distances approaching a thousand feet or more. A key advantage of the electromagnetic launcher is its rate of fire. Because the launcher can fire multiple low-cost nets per second, it can repeatedly engage a drone until one net successfully captures it. This capability also makes the system viable against drone swarms, which Hall believes represent one of the most serious emerging threats. Instead of relying on a single, high-value interceptor, the system overwhelms the problem with volume and repetition. Hall discusses the markets most likely to adopt capture-based counter-UAS systems, including airports—where a single drone sighting can shut down operations at enormous cost—prisons facing contraband delivery by drone, border security operations, and large public venues vulnerable to coordinated drone attacks. He also notes that visible counter-UAS defenses can act as a deterrent, discouraging drone operators from attempting
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