Our latest analytical report is from our Call for Expert Reports and is delivered under the Technology Observatory. The report focuses on UAV-detection technologies for law enforcement, specifically considering factors such as operational readiness and regulatory feasibility. It concentrates on detection technologies for cases when the drone doesn’t emit an RF signal and thus can’t be detected through usual detection methods, and where detection needs to take place at specific locations where there is a boundary or a limitation on the investment that can be made into the detection technologies.
Read the report: UAV Detection Technologies for Law Enforcement: Operational Readiness and Regulatory Feasibility against RF-Silent Targets at Bounded, Resource-Constrained Sites
Executive Summary
Non-cooperative drone operations are now a routine problem for LEAs at bounded civil-security sites. A growing share falls outside the assumption on which passive radio-frequency (RF) detection rests because it flies pre-programmed GNSS waypoints, is guided by onboard vision, or is controlled through a physical link. RF silence removes one detection route; it does not make the aircraft invisible to radar, EO/IR or acoustic sensing. The operators most exposed to that gap are frequently those least able to close it.
This report asks a narrower question than which modality is superior: what detection capability a budget-constrained operator obtains, over what distance, and where that distance suffices. It is a comparative review of literature, EU law, standards and public deployment evidence completed to 27 July 2026; it conducts no new trials and assesses readiness for defined configurations at defined sites, not for modalities.
Reach is primarily governed by target size. Wingspan-scaling of a related-party field evaluation gives indicative camera-only median detection distances of approximately 32–41 m for a 0.35 m platform, rising to 130–164 m for the 1.42 m object actually measured (Table 10). The source used a non-representative target and cameras tuned for avian detection; the figures bound the pattern indicatively and are not independent validation.
Converted into time, these radii define the capability. At a 40 m visible-band perimeter, a 0.35 m platform transiting at 15 m/s is exposed for under three seconds and a fast FPV platform at 35 m/s for under two. In barrier-coverage terms , such a field delivers weak, probabilistic coverage of constrained approach corridors, not strong coverage of an airspace volume. Fast approach needs a different geometry, based on acoustic nodes in depth; neither its range nor its cost is evidenced here, and characterising both, with thermal, is the principal research recommendation.
On cost, an author-supplied scenario puts the equipment allowance at approximately €16,650–€21,800, or €1,700–€2,200 per node, equipment only (daylight-hours, visible band). They are arithmetic combinations of stated line items, not validated costs, and exclude lifecycle items and the fixed wide-field cameras the pattern requires. The evidential burden differs by site: where the comparator is no capability at all, a deployment must be better than nothing; at designated critical infrastructure, where EU policy calls for multi-sensor architectures, it must demonstrate what it adds.
Three author-defined patterns are distinguished. Autonomous Optical pairs a fixed wide-field camera with an internally cued PTZ and can complete the optical search-to-classification chain unaided. Light Hybrid retains that core and adds only the modality the site’s threat model justifies. Heavy Hybrid integrates radar, passive RF, EO/IR and, where justified, acoustic sensing within a common C2 environment. No pattern is universally optimal; the report recommends comparative trials, configuration-specific acceptance testing and an RF-silent stratum in the CEN Workshop Agreement (CWA) 18150 counter-UAS test methodology before any is relied upon in procurement.
This report was prepared by the External Experts Eduardo Cermeño, Almudena Aguilera, Ana Pérez, and Jorge Abrines
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