Counter-UAS jammer and RF suppressor geolocation from orbit
Ground-deployed counter-UAS jammers broadcast across ISM bands that LEO RF-monitoring satellites can detect and geolocate. This page explains the physics, the methods, and the honest limits of doing that from orbit.
Sensors
- HawkEye 360 cluster satellites: Three-satellite formation flying in LEO at roughly 575 km altitude. Measures time-difference and frequency-difference of arrival (TDOA/FDOA) across a claimed frequency range of approximately 144 MHz to 15 GHz, covering all principal counter-UAS ISM bands. Published geolocation accuracy is cited by the company as sub-kilometre under favourable geometry, degrading toward several kilometres when elevation angle is low or the cluster geometry is poor.
- Spire LEMUR-2: Spire's LEMUR-2 buses carry software-defined radio payloads that can be tasked across a broad frequency range. The constellation exceeds 100 satellites, giving revisit intervals of roughly 90 to 120 minutes over most latitudes. Spectral monitoring and signal characterisation are primary outputs; standalone geolocation accuracy from a single satellite is limited without multi-satellite TDOA geometry.
- TDOA/FDOA multi-satellite geometry (general method): Geolocation accuracy from TDOA alone scales with the time-measurement precision, signal bandwidth, and the angular spread of the receiving cluster. Wideband jammers with fast rise-times provide sharper TDOA peaks. Narrowband spot-frequency jammers are harder to distinguish from ambient ISM traffic and require longer integration times, which increases position uncertainty.
- Passive RF survey payloads (generic LEO class): Several commercial and government LEO payloads perform wideband spectrum surveys without real-time geolocation. These are useful for detecting anomalous ISM-band occupancy patterns across a scene, flagging candidate emitter areas for follow-on tasking rather than providing a precise fix.
What a counter-UAS jammer actually broadcasts
Most commercially available counter-UAS jammers operate by flooding the frequency bands that consumer and military drones rely on for control links and video downlinks. The four principal targets are 433 MHz, 915 MHz, 2.4 GHz, and 5.8 GHz, all within internationally designated ISM bands. Some systems add GNSS denial on L1 (1575.42 MHz) and L2 (1227.60 MHz), though that function is covered separately in the GNSS jamming page in this library.
Active jammers are, by definition, transmitters. A device designed to suppress a 100 mW drone control link may itself radiate tens of watts across a broad swath of spectrum. That power density, broadcast omnidirectionally or in a wide sector, is detectable at orbital altitude if the receiving payload has sufficient sensitivity and the geometry is favourable. The jammer's own emission is its signature.
How LEO satellites turn signal timing into a map coordinate
The dominant geolocation method for ground emitters from LEO is time-difference of arrival combined with frequency-difference of arrival, known as TDOA/FDOA. When two or more satellites in close formation receive the same emission at slightly different times and with slightly different Doppler shifts, the intersection of the resulting hyperbolic and hyperbolic-frequency surfaces constrains the emitter's ground position. HawkEye 360's three-satellite cluster is the best-documented commercial implementation of this approach.
Accuracy depends on several factors simultaneously: the precision of the onboard clocks, the bandwidth of the signal being measured (wider bandwidth means sharper TDOA correlation peaks), the geometry of the satellite cluster relative to the emitter, and the signal-to-noise ratio at the receiver. For a broadband jammer radiating tens of watts across a 2.4 GHz band, TDOA conditions are relatively favourable. Published HawkEye 360 figures suggest sub-kilometre accuracy is achievable in good geometry; under poor elevation angles or with narrowband spot-frequency jammers, the position ellipse can expand to several kilometres.
A single overpass typically lasts two to four minutes over a given ground point. If the jammer is active for that entire window, a single cluster can produce one position fix per pass. Multiple passes, or coordination between separate constellation assets, can refine the estimate and confirm persistence.
The ISM-band ambiguity problem
The 2.4 GHz and 5.8 GHz bands are among the most congested radio environments on Earth. Wi-Fi access points, Bluetooth devices, microwave links, and industrial equipment all share these allocations. A satellite detecting elevated power in these bands over an urban area has no immediate way to distinguish a counter-UAS jammer from a dense cluster of Wi-Fi routers.
Several analytical strategies reduce but do not eliminate this ambiguity. Spectral shape matters: jammers typically produce a flatter, more uniform power spectral density across the band than protocol-based communications signals, which show structured bursts and guard intervals. Temporal behaviour matters too. A jammer activated in response to a drone incursion will show a characteristic onset pattern, often correlated in time with known UAS activity in the area. Geographic context is the third filter: a high-power ISM emission from a location with no civilian infrastructure, near a military checkpoint or a protected airspace boundary, is a different analytical finding than the same emission from a city centre.
Even with all three filters applied, false positives remain a genuine risk. Analysts should treat orbital RF detections as probability-weighted candidate locations, not confirmed identifications, until corroborated by other intelligence.
Frequency coverage limits of current LEO payloads
HawkEye 360 publishes coverage from approximately 144 MHz to 15 GHz, which spans all four principal counter-UAS ISM bands comfortably. Spire's SDR-based payloads are configurable across a broad range, though publicly documented tasking parameters are less specific. Neither constellation currently publishes sensitivity figures that allow independent calculation of minimum detectable emitter power at a given altitude.
The 433 MHz band presents a different challenge. At lower frequencies, the receiving antenna aperture required for high sensitivity grows, and the band is shared with a wide range of legitimate devices including weather stations, garage door openers, and amateur radio. Geolocation accuracy from TDOA also degrades at lower frequencies because the signal bandwidth available for correlation is narrower.
Revisit is the other honest constraint. At roughly 90 to 120 minutes between passes for a large LEO constellation, a jammer that is active only during a brief incursion window may not be observed at all. Persistent emitters, or those used in sustained operations, are far more detectable than episodic ones.
Conflict zones and protected airspace: what orbital monitoring adds
In conflict environments, counter-UAS jamming has become routine. NATO STO and CISA publications have both documented the proliferation of commercial and military jamming systems across contested areas, and the difficulty of attributing their use to specific actors without persistent monitoring. Orbital RF monitoring offers a form of observation that is not subject to the access constraints that ground-based monitoring faces in active conflict zones.
For protected civilian airspace, the regulatory picture is clearer. Jamming within or adjacent to airports, nuclear facilities, or other sensitive sites is illegal in most jurisdictions. An orbital detection of ISM-band jamming near a known protected airspace boundary, correlated with a flight disruption report, constitutes meaningful evidence for regulatory or law-enforcement purposes, provided the geolocation uncertainty is properly characterised in any submission.
Satellize can integrate orbital RF detections with open-source contextual layers, including airspace boundary data and known infrastructure locations, to produce structured alert reports rather than raw signal logs. The approach is similar in principle to the contextual layering used in the Tonga crop-estimation programme, where raw sensor output gains analytical value only when placed against verified ground reference data.
What this method cannot do
Orbital RF geolocation does not identify the operator or the legal authority behind a jammer. It locates an emitter. Attribution requires additional analytical steps.
It cannot reliably detect low-power directional jammers. A system that radiates only in a narrow beam pointed away from the satellite's ground track may fall below the detection threshold entirely. Handheld or vehicle-mounted units used briefly and then switched off present the hardest detection problem.
Geolocation accuracy, even under good conditions, is measured in hundreds of metres to low kilometres. That is sufficient to identify a compound, a road junction, or an airfield perimeter. It is not sufficient to identify a specific vehicle in a convoy or a rooftop in a dense urban block.
Finally, the commercial RF monitoring market is young. HawkEye 360 and Spire are the most documented providers, but neither publishes sensor specifications in enough detail for independent verification of claimed detection limits. Buyers should request scenario-specific performance assessments rather than accepting headline accuracy figures at face value.
Typical figures
| Primary frequency bands covered | 433 MHz, 915 MHz, 2.4 GHz, 5.8 GHz (ISM); HawkEye 360 published range approx. 144 MHz to 15 GHz |
| Geolocation method | TDOA/FDOA using multi-satellite cluster formation |
| Geolocation accuracy (broadband emitter, good geometry) | Sub-kilometre (HawkEye 360 published claim); degrades to several km under poor elevation or narrowband signals |
| Revisit interval | Approx. 90 to 120 minutes for large LEO constellations; single-cluster passes every few hours |
| Minimum detectable emitter power | Not publicly specified by commercial operators; practically, tens of watts broadband at 2.4 GHz is reliably detectable; handheld low-power units are uncertain |
| Dwell time per pass | Typically 2 to 4 minutes over a fixed ground point |
| Archive depth | HawkEye 360 commercial archive from 2018; Spire from approximately 2016 for some payload types |
| Delivery formats | Emitter location reports (latitude, longitude, uncertainty ellipse), spectrum occupancy logs, GIS-compatible shapefiles |
| False-positive risk (ISM bands) | High in urban areas without contextual filtering; reduced by spectral shape analysis and geographic context |
Analytics Satellize can run
| Jammer activity alert | TDOA/FDOA geolocation cross-referenced against known ISM emitter database and airspace boundary layers | Structured alert report with location, uncertainty ellipse, timestamp, and contextual flags (proximity to protected airspace, military zones) |
| Persistent emitter map | Multi-pass RF detection aggregation; temporal clustering of repeat detections at consistent coordinates | GIS layer showing candidate fixed jammer positions with detection frequency and confidence score |
| Spectral anomaly classification | Power spectral density shape analysis to distinguish broadband jamming signatures from protocol-structured ISM traffic | Classification report per detection event: probable jammer, probable communications device, or indeterminate |
| Conflict zone RF activity timeline | Time-series aggregation of ISM-band detections across a defined area of interest, correlated with open-source event reporting | Chronological activity log suitable for intelligence reporting or regulatory submission |
| Protected airspace proximity assessment | Geolocation output buffered against published airspace boundaries (ICAO data); flagging of detections within defined exclusion radii | Compliance-oriented summary report with mapped detections and regulatory context |
| Emitter persistence and mobility assessment | Comparison of geolocation fixes across multiple passes to distinguish fixed installations from mobile or episodic emitters | Analyst note with position-drift statistics and probable emitter classification (fixed vs. mobile) |
Who does the work
We can get this done for you. Satellize runs its own analyst desk and a strong science team. You do not buy a data feed and work out what it means; our people source the imagery, run the analysis described on this page, and hand you the answer with its confidence limits stated. Discuss this requirement.