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Short answer: a swarm may keep a limited formation or collective route after GPS/GNSS or radio loss by combining inertial and visual sensing with observations of nearby drones. Decentralized controllers can infer a neighbor’s motion and react locally instead of waiting for a central command. That can preserve useful group behavior, but it is not the same as a guarantee that a mission will continue under deliberate jamming.
GPS/GNSS denial and communications denial are separate failures. A drone can lose its satellite fix while still exchanging messages, or lose its radio link while continuing to sense its surroundings. Losing both removes the common position reference and much of the shared state that makes coordinated planning easier.
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What fails when a swarm is jammed?
Most multi-drone systems normally combine two different information channels:
- GNSS (including GPS): an external reference for position, velocity and time.
- Communications: a way to exchange estimated states, assignments, detections and operator instructions.
Jamming either channel creates a different engineering problem. GNSS loss means each aircraft must estimate motion relative to its starting point, nearby features or other aircraft. Communications loss means a drone cannot reliably receive another aircraft’s state or a new command. A combined attack removes both the shared coordinate reference and the normal state-broadcast mechanism.
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There is no single “jam-proof swarm” mode. The behavior that remains depends on the sensors, controller, environment, vehicle spacing, mission design and the duration of the outage.
How a swarm can coordinate without GNSS
Onboard motion estimation
Without a usable satellite fix, a flight controller can integrate inertial measurements and use cameras or other onboard sensors to estimate movement. These estimates drift over time, especially in scenes with few visual features, but they can support short-duration formation keeping or obstacle avoidance.
Horyna and colleagues describe decentralized fast cooperative flight in GNSS-denied, feature-poor environments. Their approach uses onboard mutual perception and flocking-state feedback rather than an external localization system. The same work describes estimating the states that would normally be communicated, reducing dependence on a continuously reliable network. The result is a way to keep control decisions local; it does not provide an indefinitely accurate global map.
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A drone does not always need to know its latitude and longitude if it can determine where nearby teammates are and how they are moving. Camera-based or other mutual-perception systems can provide relative bearing, range or image motion. Each vehicle can then maintain spacing, align its velocity with neighbors and avoid collisions in a local frame.
This approach is especially useful when every vehicle has a similar sensor view but no common external position. Errors remain local rather than being corrected by a shared GNSS reference, so controllers must tolerate uncertainty and avoid aggressive maneuvers when confidence falls.
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How coordination can continue without inter-drone messages
PACNav’s local-following concept
The PACNav method, described by Afzal Ahmad, Daniel Bonilla Licea, Giuseppe Silano, Tomas Baca and Martin Saska in 2023, is designed for swarms deprived of communication and external localization. Its core idea is to make decisions from local observations of relative positions.
Each aircraft examines motion history to identify a useful neighbor. Path persistence favors a neighbor whose direction has remained consistent. Path similarity helps identify aircraft moving along a common route. The drone can select that neighbor as a target to follow while a reactive collision-avoidance layer handles other aircraft and obstacles.
The authors summarize the design this way: “Similarly, PACNav relies only on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems.” In practical terms, that means local inference can replace some explicit state broadcasts; it does not mean every drone knows the full swarm plan.
What “decentralized” actually means
In a centralized architecture, a ground station or lead vehicle computes assignments and sends updates. In a decentralized architecture, each aircraft runs its own controller and acts on local sensor data, possibly with occasional messages when a link is available. Decentralization removes a single point whose failure would stop the entire group, but it also makes global optimization, conflict resolution and mission changes more difficult.
A decentralized swarm may therefore preserve a pattern, follow a local flow or avoid collisions while being unable to accept a new target, confirm every vehicle’s status or prove that all aircraft share the same plan.
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Three jamming scenarios and the expected behavior
| Failure | What remains available | Likely coordination strategy | Main limitation |
|---|---|---|---|
| GNSS denied; radio works | Neighbor messages, inertial sensors and onboard perception | Exchange relative or estimated states while replacing GNSS with local odometry and mutual sensing | Position estimates drift and may not agree across vehicles |
| Radio denied; GNSS works | Satellite position and local sensors | Continue preplanned behaviors or use onboard collision avoidance | No reliable assignments, health reports or operator updates |
| GNSS and radio both denied | Inertial/visual sensing and observations of nearby aircraft | Decentralized flocking, local following and reactive avoidance | Short-range, uncertain and environment-dependent behavior |
| Operator link denied; local links survive | Inter-drone coordination and onboard mission logic | Continue an authorized contingency plan and share local state | The swarm may remain coordinated but cannot receive new human instructions |
What has actually been demonstrated?
Fast cooperative flight in GNSS-denied environments
Horyna et al. reported real-world experiments of decentralized fast cooperative flight without external localization, including a communications-reduced variant. The available record does not establish a general benchmark for operation under active radio-frequency jamming. It demonstrates particular algorithms and test conditions, not universal reliability.
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PACNav included simulation and a natural-forest experiment with four UAVs. In that demonstration, the group reached its goal at 300 seconds. The experiment also showed why relative sensing is not effortless: trees and other aircraft sometimes occluded the camera view and affected relative-position estimates. The 300-second result belongs to that setup and should not be read as a swarm-wide success rate.
A funded GPS-denied surveillance concept
A 2021 US Small Business Innovation Research award titled “Cooperative Unpredictable Motion for Surveillance of Uncertain GPS-Denied Environments” proposed cooperative surveillance software. Its stated measures included relative localization and minimizing communication events. An award records funded development intent; it does not prove that a fielded system met those goals.
A proposed satellite fallback
TRL Space Systems’ ASCUS project describes autonomous swarm software paired with a dedicated satellite-communications link for cases in which terrestrial communications fail or are intentionally jammed. The project states that a demonstration involving one communications satellite and three UAV types is planned for 2028. That is a development target, not an independently verified current capability or a guarantee that satellite links remain available in every contested environment.
Why the environment matters as much as the algorithm
Visual occlusion
Relative localization requires a usable view of teammates. Trees, buildings, terrain and another aircraft can hide a neighbor or create ambiguous image measurements. A controller should lower speed, widen spacing or switch to a safer fallback when confidence in the relative estimate drops.
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Feature-poor scenes
Visual-inertial motion estimation is harder over water, uniform ground, haze or darkness. Few trackable features mean more drift and fewer opportunities to correct it. Horyna et al. specifically address feature-poor GNSS-denied flight, but their experiments do not establish identical performance across all landscapes or weather.
Sensor and estimation uncertainty
Every local estimate has noise and delay. If each drone reacts strongly to a slightly different neighbor position, the group can oscillate or split. Practical controllers therefore need bounded acceleration, conservative separation rules, confidence thresholds and a defined behavior for lost detections.
Swarm density and obstacles
More aircraft provide more potential neighbors but also more occlusion and collision risk. Reactive avoidance must account for drones that are not transmitting and may be moving unpredictably. A method that works in open air may require different spacing and speed limits in a forest or urban canyon.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A useful fallback architecture
A resilient design treats jamming as a set of operating modes rather than a binary “works or fails” condition. A typical sequence is:
- Detect the outage. Monitor GNSS integrity, radio packet loss, timing quality and sensor-confidence scores. Do not wait for a complete crash in the position estimate.
- Freeze unsafe objectives. Stop accepting new long-range assignments and limit speed or acceleration while the controller determines which sensors remain trustworthy.
- Switch the reference frame. Use inertial and visual odometry, then relative observations of nearby aircraft, instead of treating a missing GNSS fix as zero position error.
- Move to local coordination. Maintain separation, follow a persistent neighbor or preserve a preauthorized flocking pattern. Each vehicle should be able to make the decision without a central broadcast.
- Manage confidence. When a neighbor disappears behind an obstacle or the estimate diverges, increase spacing, slow down, hover or leave the formation according to the mission’s safety rules.
- Use an approved communications fallback. If an alternate link such as a satellite channel is part of the system, treat it as a separate, qualified capability. A proposed fallback is not proof of availability during a real attack.
- Recover deliberately. Once GNSS or communications return, reject stale states, re-establish a common time and frame, reconcile vehicle identities and only then resume global assignments.
How to judge a claimed “jam-resistant” swarm
Ask for evidence along five axes rather than accepting a marketing label:
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- Localization input: Does the system require GNSS, inertial/visual odometry, relative observations or a combination?
- Communication dependence: Does it need continuous state exchange, intermittent messages, inferred neighbor states or no explicit messages?
- Coordination structure: Is there a central supervisor, a lead vehicle or independent local controllers?
- Test environment: Were trials conducted in open air, a forest, a feature-poor scene, darkness or with deliberate interference?
- Recovery behavior: What does a vehicle do when it loses a neighbor, its operator link or confidence in its position?
Also separate simulation, laboratory demonstrations, field experiments, funded proposals and planned demonstrations. They answer different questions. A simulation can show that a controller is internally stable; a forest trial can reveal occlusion; neither alone proves reliable operation against an adversary’s jammer.
Can drones fly without GPS?
Yes, for a limited task and time, if they have suitable onboard sensing and a controller designed for degraded navigation. They may estimate motion, avoid obstacles and keep a local formation without a satellite fix. Accuracy generally degrades with time and poor visual conditions, and the aircraft may not know its absolute position.
“Without GPS” therefore does not mean “without navigation.” It means navigation is being performed by other sensors and references, with different error bounds.
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Can drones communicate without a signal?
Not in the literal sense: communication requires some physical signal. A swarm can, however, coordinate without a functioning radio data link by observing neighbors and inferring their motion. PACNav is an example of this local-observation model. It provides less information than a working network and cannot replace messages for every task, such as changing a target or reporting a complete health state.
What these methods cannot promise
The documented results do not support a universal claim of uninterrupted mission performance, immunity to active jamming or safe operation in every environment. Local sensing can preserve collective motion, but occlusion, feature-poor scenes, estimator drift and collision risk remain. A satellite fallback described as a future project is not the same as a deployed, independently tested anti-jam service.
The defensible conclusion is narrower and more useful: carefully designed swarms can degrade gracefully by shifting from external and shared references to onboard estimates and local observations. They should be treated as operating under uncertainty, with explicit speed, spacing, mission and recovery limits.
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