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Short answer: a swarm can preserve limited collective flight after GPS/GNSS or radio loss by combining inertial and visual sensing with observations of nearby aircraft. Each drone estimates its own motion, detects neighbors, and makes local decisions instead of waiting for a central controller. Losing GNSS and losing communications are different failures, however, and losing both at once is substantially harder.
Methods demonstrated in research can maintain formation, choose neighbors to follow, and avoid collisions in defined experiments. They do not establish that any swarm is universally “jam-proof” or able to complete every preplanned mission while an adversary is actively jamming its links.
GPS loss and communications loss are different problems
People often use “GPS is jammed” and “the drones cannot communicate” interchangeably. A resilient swarm treats them as separate dependencies:
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|---|---|---|---|
| GNSS/GPS denial | A common satellite-based position and time reference | Inertial sensors, cameras, lidar or other local sensors, and radio links | Estimating position and velocity without a trusted global coordinate |
| Inter-drone communication loss | State broadcasts, assignments, and explicit neighbor messages | Onboard sensing and autonomous controllers | Inferring neighbors’ motion and intent from local observations |
| Operator-link disruption | Remote commands, telemetry, and supervision | Preloaded autonomy and possibly an alternate link | Continuing safely without assuming that a human can intervene |
| Combined GNSS and communications denial | Both the external reference and explicit state exchange | Only onboard estimation, passive or active local sensing, and any surviving fallback link | Keeping a coherent group while estimation errors and uncertainty grow |
A drone can therefore lose satellite positioning while still exchanging data with its peers, or retain good inertial and visual estimates while its radio is unavailable. Controllers designed for one case should not be described as solving the other.
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How a swarm navigates without GNSS
Onboard motion estimation
When no usable GNSS fix is available, a drone estimates changes in position and attitude from sensors such as inertial measurement units and cameras. The estimate is relative and accumulates uncertainty over time; it is not the same as receiving an independently verified latitude and longitude.
Horyna and colleagues describe decentralized fast cooperative flight in GNSS-denied, feature-poor environments. Their approach uses onboard mutual perception and flocking-state feedback so aircraft can use what they observe about one another instead of depending entirely on an external localization system. The authors also describe multi-robot state estimation and a communications-reduced variant that estimates states which would otherwise be exchanged directly.
Mutual perception supplies a local reference
If one aircraft can detect another’s relative position or motion, that observation constrains the first aircraft’s estimate. Several aircraft can maintain a local geometric relationship even though none has a reliable global coordinate. This is useful for staying together, passing through a corridor, or following a moving group, but it does not automatically reveal the swarm’s absolute location on a map.
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Relative sensing is sensitive to the environment. A camera must see a neighbor clearly enough to estimate its location and movement. Lighting, texture, dust, weather, range, sensor field of view, and occlusion all affect the result. A controller must carry uncertainty estimates and reduce speed, increase spacing, or abort a maneuver when confidence falls.
How coordination works when drones cannot exchange messages
Local decisions instead of a central commander
A decentralized swarm gives each aircraft a controller that uses its own measurements. It does not wait for a continuous stream of positions, headings, task assignments, or health reports from every other member. The group behavior emerges from the same local rules running on each aircraft.
PACNav, presented by Afzal Ahmad, Daniel Bonilla Licea, Giuseppe Silano, Tomas Baca, and Martin Saska in 2023, is an example designed for swarms deprived of communication capabilities and external localization. The authors summarize the idea as relying only on local observations of relative UAV positions. In practical terms, each aircraft must decide whom to follow and how to avoid collisions using what it can currently observe.
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Path persistence and path similarity
PACNav uses two local cues to select a neighbor:
- Path persistence: a neighbor whose direction has remained consistent is favored over one making erratic changes.
- Path similarity: neighbors moving in a common direction are treated as members of the same flow or subgroup.
The aircraft can select a neighbor as a target to follow while a reactive collision-avoidance layer steers around other aircraft and obstacles. Because the rule is local, a drone does not need a global leader or a complete table of the swarm’s state. The trade-off is that local inference can be wrong, especially when a neighbor disappears behind an obstacle or another aircraft.
What “no signal” really means
“No signal” can mean no operator connection, no inter-drone radio, or active interference that corrupts or blocks a particular band. A vision-based controller may continue to estimate a neighbor without receiving a radio packet, but it cannot recover information that is outside its sensors’ range. It also cannot guarantee that every member has the same mission plan or knows about hazards detected by an unseen aircraft.
When the operator link is jammed
Autonomy can let a swarm continue a bounded behavior after losing its ground connection, but the sources reviewed do not establish a universal way to restore an operator link under active jamming.
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TRL Space’s ASCUS project describes autonomous swarm software paired with a dedicated satellite-communications link as a fallback when terrestrial communications fail or are intentionally jammed. The project states that a demonstration with one communications satellite and three UAV types is planned by 2028. That is a development target, not an independently verified capability available to all swarms today. A satellite link can also be denied, delayed, capacity-limited, or unavailable at low elevation, so it should be treated as one layer in a resilience plan rather than a guarantee.
Architectures compared by the dependency they remove
| Architecture | Localization input | Communication dependence | Typical strength | Important limitation |
|---|---|---|---|---|
| GNSS-centered formation | Shared GNSS position and time | Usually continuous state exchange | Simple global coordinates and predictable mission planning | Fails when GNSS is denied and may depend on a functioning network |
| Onboard inertial/visual estimation | IMU plus cameras or other onboard sensors | Can operate with intermittent exchange | Preserves local motion estimates without satellites | Drift and scene-dependent perception errors accumulate |
| Relative-observation swarm | Observed positions and motion of nearby aircraft | Explicit messages can be minimized or removed | Supports collective motion from local information | Occlusion, range, lighting, and missed detections break the estimate |
| Centralized supervisor | Usually a global or fused map | High: aircraft depend on a controller or network | Efficient task assignment and broad situational awareness | A single link or node can become a failure point |
| Satellite fallback link | Depends on the aircraft’s navigation sensors and link design | Provides an alternate external path | May restore supervision when terrestrial links fail | ASCUS is a project with a planned 2028 demonstration, not a general fielded guarantee |
No approach is a universal winner. The relevant choice depends on whether the mission prioritizes absolute navigation, group cohesion, low radio emissions, operator control, or safe return when confidence drops.
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What has actually been demonstrated
PACNav forest experiment
The PACNav paper reports simulation and a real-world experiment in a natural forest using four UAVs. In that demonstration, the group reached its goal at 300 seconds. The result belongs to that experiment and should not be converted into a general success rate for swarms or a performance guarantee under hostile jamming.
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The same experiment exposed a practical weakness: trees and other aircraft sometimes occluded the camera’s view and affected relative-position estimates. A swarm that relies on visual neighbors therefore needs behaviors for reacquisition, spacing, speed reduction, and safe termination when observations become unreliable.
Horyna et al. cooperative flight
Horyna et al., writing in the 2024 issue of IEEE Robotics and Automation Letters, report real-world experiments of decentralized fast cooperative flight without external localization and a variant that reduces communications. Their work demonstrates particular algorithms and conditions; the available record does not provide a cross-system benchmark for operation during active radio-frequency jamming.
US SBIR cooperative-surveillance award
A 2021 US Small Business Innovation Research award titled “Cooperative Unpredictable Motion for Surveillance of Uncertain GPS-Denied Environments” proposed relative localization and fewer communication events. An award record documents funded research intent and proposed measures, not proof that a fielded product achieved them.
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A practical resilience sequence for swarm designers
- Define the failure separately. Test GNSS denial, inter-drone radio loss, operator-link loss, and the combined case. A system that survives one should not be assumed to survive all four.
- Choose the minimum behavior that must continue. Examples include holding a safe pattern, following a visible neighbor, leaving the area, or landing. Do not claim that full mission execution is preserved unless it has been tested.
- Build a local state estimate. Fuse inertial data with available visual or other onboard observations, and attach an uncertainty value to position, velocity, and neighbor identity.
- Implement neighbor selection locally. Use persistence and similarity of observed paths, as in PACNav-style logic, while rejecting targets with inconsistent motion or low confidence.
- Keep collision avoidance independent. A formation or following rule should never override an immediate obstacle or aircraft-avoidance response.
- Set confidence thresholds and exits. If a drone cannot see enough neighbors, uncertainty grows beyond a limit, or its energy reserve reaches a threshold, it should switch to a defined safe action rather than continue blindly.
- Add fallback connectivity only where justified. An alternate terrestrial path or satellite link may restore supervision, but its availability, latency, coverage, and susceptibility to interference must be measured for the intended region.
- Validate in representative environments. Include feature-poor scenes, tree or aircraft occlusion, changing light, obstacles, sensor dropouts, and delayed or missing messages. Report the conditions and failure cases with every performance number.
Common failure modes and mitigations
| Symptom | Likely cause | Mitigation |
|---|---|---|
| Neighbors disappear from the estimate | Occlusion, range, glare, or a narrow sensor field of view | Increase spacing, slow down, change viewpoint, reacquire before continuing, or enter a safe mode |
| Group slowly spreads or drifts | Uncorrected inertial error or inconsistent local estimates | Use mutual observations when available, bound speed and duration, and monitor uncertainty |
| Drone follows the wrong aircraft | Path similarity is ambiguous or detections are misidentified | Require persistent identity and direction agreement; reject abrupt or low-confidence switches |
| Collision-avoidance behavior fragments the group | Immediate obstacle avoidance conflicts with formation goals | Give safety constraints priority, then allow regrouping only after clear observations return |
| Operator cannot confirm mission status | Ground link is jammed or unavailable | Use preplanned autonomy, onboard event logging, and a tested alternate link where appropriate |
Can drones fly without GPS?
Yes, for bounded tasks and environments, a drone can estimate motion without GNSS using onboard sensors and relative observations. The practical question is how long the estimate remains accurate, whether the environment supplies enough visual or geometric features, and what the aircraft does when confidence degrades. “Can fly” does not mean “can maintain surveyed coordinates indefinitely” or “can complete any autonomous mission under jamming.”
Can drones communicate with one another without a radio signal?
They cannot exchange digital messages without some communication channel, but they can coordinate limited behavior through sensing. A drone can infer that another aircraft is nearby, determine its relative motion, and apply a shared local rule without receiving a packet. That is observation-based coordination, not full information sharing. Hidden aircraft, unseen hazards, task changes, and health data still require a surviving channel or an onboard policy that handles the missing information.
Safety, claims, and expectations
Terms such as “jam-proof,” “GPS-independent,” and “fully autonomous” are too broad unless tied to a tested configuration, environment, duration, and threat model. The PACNav and Horyna results concern specific algorithms and experiments. The ASCUS satellite fallback is described as an R&D project with a planned demonstration. None of these sources establishes uninterrupted performance for every commercial swarm, nor do they remove aviation rules, geofencing obligations, or the need for a safe contingency when navigation and supervision become uncertain.
Quick Recap
Sources discussed
- Horyna et al., “Fast Swarming of UAVs in GNSS-Denied Feature-Poor Environments Without Explicit Communication,” IEEE Robotics and Automation Letters, 9(6), 5284–5291, 2024.
- Afzal Ahmad, Daniel Bonilla Licea, Giuseppe Silano, Tomas Baca, and Martin Saska, “PACNav: A Collective Navigation Approach for UAV Swarms Deprived of Communication and External Localization,” 2023.
- US Small Business Innovation Research, “Cooperative Unpredictable Motion for Surveillance of Uncertain GPS-Denied Environments,” award year 2021.
- TRL Space Systems, ASCUS project description, including its stated 2028 demonstration plan.
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