By Yohai Schwiger
Innoviz this week announced another partnership in the counter-unmanned aerial systems (Counter-UAS) market, this time with Israeli company AeroNous, developer of an open command-and-control (C2) platform designed to protect critical infrastructure, strategic facilities and sensitive sites. Under the agreement, Innoviz’s LiDAR technology will be integrated into AeroNous’ C2 platform to enhance real-time localization by providing highly accurate 3D positioning of aerial targets.
At first glance, this appears to be just another integration announcement. Viewed alongside the company’s recent string of partnerships, however, it points to a much broader shift in how modern Counter-UAS systems are being designed.
The AeroNous announcement follows several similar collaborations unveiled in recent weeks. Regulus is integrating Innoviz LiDAR to improve drone tracking in complex environments; Givon Defense is using it to enhance precision localization; Cogniteam has incorporated it into a new AI-powered aerial target classification module; and AeroNous is now embedding it within an open C2 platform. While these are different companies addressing different applications, the announcements repeatedly emphasize the same concepts: Localization, Perception, 3D Spatial Awareness, Classification and Situational Awareness.
Taken together, these partnerships suggest something more significant than Innoviz’s expansion into defense. They highlight the emergence of a new generation of Counter-UAS architectures.
From Single Sensors to Multi-Sensor Perception
Until only a few years ago, most counter-drone systems were built around a single primary sensor. In some cases it was radar for initial detection; in others, electro-optical cameras or RF sensors capable of identifying communication links between a drone and its operator. Once the target was detected, the system activated an appropriate jammer or interceptor.
Today’s battlefield has changed dramatically.
Small FPV drones, autonomous drones operating without RF links, drone swarms and low-altitude flight through dense urban environments have exposed the limitations of that approach. The challenge is no longer simply detecting that an object exists in the air. Modern systems must rapidly determine what the object is, precisely where it is located, where it is heading and how its trajectory is likely to evolve over the next few seconds.
As a result, Counter-UAS platforms are increasingly adopting an architecture that closely resembles autonomous driving perception systems.
Rather than relying on a single sensor, they build a sensor fusion layer that combines multiple information sources. Radar provides long-range detection and tracking; RF sensors identify communication signals when available; EO/IR cameras contribute visual identification and classification; AI algorithms correlate the incoming data; and the command-and-control system produces a unified operational picture from which engagement decisions are made.
Within this architecture, LiDAR plays a distinctly different role from the other sensors.
Across nearly every Innoviz announcement, the technology is described not primarily as a detection sensor but as a localization and perception sensor. In other words, its purpose is not simply to indicate that an aerial threat exists, but to provide the system’s spatial perception layer.
LiDAR contributes information unavailable from the other sensors: a detailed 3D point cloud of the environment, precise spatial coordinates, distance, altitude, shape and motion. This enables highly accurate localization, continuous target tracking and real-time positional updates for the C2 platform.
In Cogniteam’s implementation, for example, the point cloud also serves as the foundation for an AI module capable of distinguishing drones from birds and other airborne objects, significantly reducing false alarms.
In this sense, LiDAR is becoming the perception layer of modern Counter-UAS systems. It does not replace radar, cameras or RF sensors. Instead, it complements them by providing the spatial information upon which sensor fusion and tracking algorithms depend.
It is also noteworthy that most of Innoviz’s recent partners are not building interceptors. AeroNous, Cogniteam and Regulus focus on software layers, perception engines and command-and-control platforms. This reflects a broader shift in the industry, where competitive advantage is increasingly moving away from the interceptor itself and toward the software architecture that fuses multiple sensors into a single operational model of the airspace.
Why LiDAR? Lessons from Autonomous Driving
The comparison with autonomous vehicles is difficult to ignore.
The automotive industry reached a similar conclusion years ago: no single sensor can safely perceive the surrounding environment. Radar excels at measuring range and velocity but provides limited shape information. Cameras deliver rich visual context but depend heavily on lighting conditions. LiDAR adds precise three-dimensional mapping. Sensor fusion combines all of these inputs into a unified representation of the world, enabling autonomous driving decisions.
Counter-UAS systems are now following a remarkably similar path.
The difference is that, instead of detecting pedestrians and vehicles, they must identify small, fast and often autonomous drones navigating among trees, buildings and power lines. Here too, success depends on maintaining an accurate, continuously updated 3D representation of the environment.
This may also explain why Innoviz’s technology appears particularly well suited to these applications.
For more than a decade, the company developed its LiDAR platform for one of the world’s most demanding perception challenges: autonomous driving. Beyond the optical hardware itself, Innoviz built an extensive software stack that includes firmware, signal processing, point cloud processing, perception algorithms and object-level data generation.
Rather than outputting millions of raw laser points, the system can produce structured object data—including position, velocity and direction—ready for integration into higher-level command-and-control software.
The hardware itself offers additional advantages. Automotive-grade LiDAR must operate continuously under harsh environmental conditions while maintaining high reliability, low power consumption and resistance to vibration and temperature extremes—qualities equally valuable in military applications.
Ultimately, Innoviz’s recent partnerships reveal less about the LiDAR market than about the future direction of Counter-UAS technology.
The next generation of systems is no longer being built around a single sensor or a single interceptor. Instead, it relies on a multi-sensor perception architecture in which each component contributes a different layer of information to a unified operational picture.
If the previous objective was simply to detect a drone, the new objective is to construct an accurate, continuously updated three-dimensional model of the airspace—and LiDAR is rapidly becoming one of the key building blocks of that architecture.