A squad of infantry dismounts their infantry squad vehicle and begins moving toward the objective. As the soldiers approaches their assault position, an alert pings throughout the squad’s command-and-control team awareness kit devices: “HOSTILE GRP 2 DRONE DETECTED, 1.7km, 045°, TRACK-ID 2112.” The drone’s location populates as a red dot on the map, along with its ID, and the text message drops from the screen. The drone was detected by an acoustic sensor from a forward multifunctional reconnaissance company and a small panel radar mounted on an infantry squad vehicle from an adjacent platoon. Although the sensors are distributed among separate echelons, the drone tracks from each sensor are fused into a single track and populated on the squad’s team awareness kit devices. The battalion headquarters sees the same threat and directs its multipurpose company to launch a first-person-view drone with the task of destroying Track 2112. Within seconds, the friendly drone is launched, and the hostile drone is destroyed. As the infantry squad approaches the assault position, the hostile track drops off the map, and a text alert—“Track 2112 destroyed”—is sent throughout the squad.
This is the power of deliberately architected networks and sensor fusion: fast, efficient, shared awareness. One track, one threat, one decision, one common operational picture. As drones proliferate across every theater, this kind of seamless, fused detection will define the difference between successful operations and losses of combat power.
Understanding sensor fusion and network architecture isn’t optional to solve the C-UAS (counter–unmanned aircraft system) problem—it’s the entry fee to the professional conversation. To repurpose a well-known aphorism, amateurs will highlight the newest kit on the market, while professionals will discuss network integration and sensor fusion.
There are two critical tasks the Department of Defense must accomplish to solve its current C-UAS challenges: first, prescribing a common command-and-control (C2) system for all services, and second, implementing a network architecture to share sensor and effector data from the tactical to the strategic levels. Science and technology bureaucrats beware: The good old days of implementing bespoke systems on hub-and-spoke networks are ending, as leaders become more aware of our archaic and siloed air defense architectures.
Beyond New Gadgets: The Need for Seamless C2 Networks
The early response to drone threats has been to throw hardware at the problem—hundreds of millions spent on handheld jammers, exquisite radars, and costly interceptors. Each comes with limitations, but the bigger issue is that they operate independently, on bespoke networks. These siloed tools offer little beyond point-defense solutions. They lack the ability to tap into the abundance of joint and strategic sensor data to enhance their functions, making them blind to the broader battlefield and disconnected from the fight around them. As drones proliferate, the assumption that newer or pricier tech is the answer must be challenged. That thinking has led to the fragmented set of black-box capabilities that defines much of the C-UAS ecosystem today across DoD. The mindset that the solution is to throw kit at the problem must go. A new culture of stitching sensors and effectors together is the only way forward.
Building the layered defense vision depicted on Pentagon OV-1 charts takes more than a fictional green ring above systems that falsely illustrates to senior leaders that everything is connected. The department requires a common C2 system and network to a globally accessible architecture that can rapidly onboard new technology, fuse sensor tracks across echelons, and maintain common track identity. This means integrating multiple sensors and effectors onto real networks to deliver a single common operational picture on a tailored user interface. When two sensors see the same drone, the C2’s common operational picture across the network must show one track—not two. And that fused track needs to be shared in real time across the force so any shooter, at any level, can respond with precision.
No single sensor or shooter is sufficient on its own; success comes from linking assets—sensing, deciding, and acting—in a coordinated web. The term survivable should mean that when a sensor, effector, or C2 system is lost in combat, we should not lose an entire capability.
The same principles of networking and fusion that apply to maneuver formations also extend to the defense of the homeland. Linking sensors and effectors across Department of Defense installations—and integrating those with interagency systems—shouldn’t be like a scene out of a futuristic Hollywood thriller. The technology to do this has existed for years. However, our infatuation as a department with chasing kit has caused us to miss the forest for the trees when it comes to the C-UAS mission.
Want to solve the C-UAS problem? First, let’s start by having a basic understanding of the essential tasks: transport, fusion, and track management.
Transport is the Backbone
Establishing robust transport to connect diverse sensors and effectors into a unified network is a foundational, yet complex, challenge. Without reliable data transport modes, sensor fusion and cooperative engagement leveraging any effector is not possible. The critical hurdle lies in the network plumbing—the infrastructure that allows combat formations to see the same thing in real time. This is why sensors and effectors at the operational and strategic level must live on actual live networks—not a joint data network, integrated fires network, or some other bespoke enclave that is closed off from reality. Networks must enable a cooperative engagement capability for land forces—both at home and abroad. Strategic-level sensor data needs to get back down to the tactical level, and tactical data needs to cue strategic-level sensors and effectors—it’s a two-way street.
Transport is the starting point. Sensor data must move over existing paths to populate C2 nodes with drone tracks. For dismounted infantry or dispersed formations, this typically requires software-defined radios already burdened with voice and operational traffic. At the Army brigade echelon and below, bandwidth becomes a precious and often limited resource, particularly as the number of sensors and effectors increases. In a recent C-UAS exercise in Germany, Project Flytrap, a US Army platoon found 70 percent of its bandwidth consumed by sensor data alone.
Sensors continuously transmit track data—bearing, altitude, range, speed, and time. Higher-fidelity sensors, capable of detecting at longer ranges or generating precise, high-quality track data, will consume more bandwidth, and require lower latencies to enable fire control from exquisite effectors (Coyote, Roadrunner, etc.). Selecting the right sensor at each echelon requires balancing fidelity required (track quality), efficiency, and—most importantly—network capacity. Leaders in the field, and those fielding the kit, must thoroughly understand the bandwidth available for the organization and avoid overwhelming it with high-volume data from sensors that the network cannot support. In short, at the tactical edge, bandwidth is currency.
Sensor Fusion Explained
Sensor fusion is the alignment and merging of detections from multiple sensors into a single object, or track. If five sensors see the same drone, C2 systems across the network must display one track—not five. To stitch air tracks together, sensor fusion requires three things. First, there must be temporal alignment. All sensors, effectors, and C2 nodes must operate on a shared clock). Milliseconds matter for high end effectors. Second, the system must have spatial alignment. Because each sensor has its own frame of reference, fusion engines must translate local sensor detections into a common grid using GPS or inertial measurement unit data. If we put a sensor on a platform or a person, we must ensure we have a way to tell a fusion engine where it is. Finally, deconfliction algorithms are necessary. Techniques like dynamic time warping help correlate data streams with varying latency or reporting intervals. Selection of algorithms at echelon will be important based on compute capability, types of sensors being fused, and most importantly, the track quality requirement.
Ultimately, fusion is a continuous process of hypothesis and resolution: which detections should be grouped into a single track and which should remain distinct. Everything from filtering noise, biological tracks (e.g., birds), and even trash factor into track fusion. Getting this right is essential to avoid mirror tracks, or tracks that clutter the air picture and complicate weapon-target pairing. At scale, fusion enables a common operational picture where the data reflects the battlespace as it truly is—not a fractured collection of guesses.
The Importance of Universal Track Management
Fusion at echelon makes universal track identity management a complex data brokerage problem. Each track must have a unique ID—once assigned, it is maintained or updated across echelons. When a drone is detected by multiple sensors at different echelons, the output of network fusion should result in the same track number.
In an ideal network, a drone detected by a forward-deployed squad sensor will carry the same track number as when it is sensed by brigade, division, or joint-level assets. This continuity is the essence of universal track management—ensuring a single track ID persists across different sensors, C2 nodes, and operational levels.
However, maintaining common identity is especially challenging in a federated network where each echelon fuses its own sensor data locally. Without a shared and enforced track management protocol, the same drone may be assigned multiple IDs by different nodes, resulting in duplicated tracks. These duplicates not only distort the air picture but hinder effective engagement by obscuring which track is valid and which effector should respond.
The consequences are operationally significant. If tactical sensors fuse tracks to refine targeting for long-range interceptors, or if strategic sensors cue short-range effectors, inconsistent track IDs break the chain of custody between sensors and effectors. Coordinated defense becomes guesswork—and weapons cannot be paired to intercept.
Solving this requires robust protocols for track number assignment, deconfliction, and reconciliation across the enterprise. It also demands that track identity management be treated as a core function of C-UAS C2—not an afterthought.
The Path Forward
After years of resources wasted on bespoke kit, and still without an existing solution to tie in all sensors and effectors on a single network across services, it should be apparent that C-UAS isn’t about buying better hardware. It’s about establishing a network architecture on common C2. Fielding sensors and effectors that cannot pass track data across echelons, commands, and services is a recipe for failure. Likewise, continuing to forgo fusion, transport, and track identity will leave formations with siloed systems that can’t leverage the potential of the whole infrastructure.
What’s needed now is a deliberate shift in culture: from hardware-first solutions to architecture-driven integration on a common C2 across the department. The technology is here. The concepts are tested. Senior leaders not only understand the challenge, but are actively driving the enterprise toward scalable, connected, and lethal solutions.
To start down the path to success, and a future where sensors and effectors are parts of a complementary ecosystem, there are five immediate actions DoD should take.
- Select a common C-UAS C2 for all services. The US Army, the DoD executive agent for C-sUAS, should immediately prescribe the C-UAS C2 system to be used across the department. The C2 system must have a user interface that is intuitive for all occupational specialties, have a web-based, cloud-enabled architecture, and be capable of over-the-air updates for all systems, sensors, and effectors.
- Create, publish, and manage open-source APIs for integration of C-UAS. The department must define and own the APIs—the application programming interfaces that allow different systems to interact—connecting all sensors, effectors, and C2. This should happen now. DoD’s API technical guidance reinforces that government-owned interfaces are essential to prevent vendor lock-in and ensure interoperability across programs. Without government-controlled, open APIs, each new sensor or effector requires bespoke integration, slowing fielding and increasing cost.
- Ensure a pub/sub engine at the edge. A pub/sub (publish/subscribe) engine at the edge and at echelon is essential to stitch together the diverse sensors and effectors. It enables real-time data sharing and track fusion across all sensors—allowing effectors to act on a coherent, shared threat picture. Without this layer, each sensor-to-effector link becomes a bespoke integration problem, slowing response and scaling. A common pub/sub backbone ensures modularity, speed, and interoperability across formations and platforms.
- Prioritize remote sensor tasking. This allows sensors and effectors to be controlled as needed across the network, improving track quality, creating an ecosystem of survivability, and enabling remote fire control and distributed weapon pairing.
- Emphasize remote fire control and engagement. Any C2 node with permissions must have the capability to launch an engagement with any effector on the network, using data from any sensor with sufficient track quality. This removes the need for dedicated sensor-weapon pairings on hub-and-spoke networks, which limit flexibility and create single points of failure. It enables faster targeting decisions, expands coverage, and allows for continued operation even if some sensors or nodes are lost. This approach replaces the traditional hub-and-spoke model with a more distributed and efficient system.
The drone threat is here to stay, and it has already outpaced our current capabilities. We won’t solve it by throwing more hardware or isolated systems at it. Victory in the C-UAS fight will go to the side that builds faster, more integrated networks—where any sensor can feed a common operational picture and any shooter can act on it. Sensor fusion, transport, and track identity aren’t back-end tasks; they are foundational elements of the overall C-UAS strategy. The technology exists. What’s needed now is DoD to prioritize common C2 and network architectures that enable true interoperability. We don’t need more kit—we need smarter networks that sense, decide, and act as one. The kill chain starts with the network. Build it or lose the fight.
Major Anthony Padalino is the executive officer to the US Army chief technology officer, where he assists planning and execution across Army transformation priorities, including counter-UAS. Previously, he was deployed to Al Asad Air Base with the 10th Mountain Division, where his unit achieved the highest recorded number of one-way unmanned aircraft system intercepts in US Army history. He is a key contributor to the Army’s ongoing C-UAS strategy, driving operational integration of sensors, effectors, and C2.
The views expressed are those of the author and do not reflect the official position of the United States Military Academy, Department of the Army, or Department of Defense.