Drone Detection with Radar

01 · The problem

Small non-cooperative drones can survey enterprise sites, disrupt operations or approach sensitive installations. The customary responses – a visual check by site security, a look at existing camera feeds, and a police report if in doubt – all presuppose that someone noticed something.

Each available technical alternative has a hard limit:

Remote ID only covers drones that actually broadcast their identification.
Passive RF detection needs an exploitable radio link between drone and pilot. A drone flying an autonomous waypoint mission stays invisible.
Cameras need visibility, correct pointing and sufficient object size in frame.
High-performance specialist radars solve the sensing problem but are economically out of reach for many private sites.

An industry survey on drone protection in Germany lists three main reasons why companies take no protective measures at all: cost, lack of expertise, and legal uncertainty. The barrier to adoption is therefore not primarily missing technology.

02 · Why radar

Can radar detect autonomous drones with no radio link?

Yes – by virtue of the measurement principle. Radar measures the signal reflected by the object and does not depend on the drone transmitting anything. It additionally measures range and radial velocity directly, which is decisive for tracking and prioritisation, and it works independently of visible light.

That is a property of the sensing principle, not a performance promise. Whether an echo becomes a stable, correctly assessed target candidate depends on range, radar cross section, altitude, motion profile and the clutter present at the site.

03 · The Ozense approach

01

Detection

The radar continuously transmits chirps in the 77 GHz band. Range and Doppler FFTs turn the echoes into a range-velocity map, from which a CFAR detector produces a candidate list. An adaptive clutter filter suppresses learned static scene content without indiscriminately deleting low-motion target content along with it.

Range-Doppler FFT
OS-CFAR detector
Adaptive clutter filter

02

Tracking

Individual detections are not yet a target. A Kalman-based tracker links them over time, estimates position and motion state, and bridges short dropouts. Only track stability, motion consistency and observation time turn raw detections into a credible candidate.

3D tracker with acceleration model
Track promotion across several frames
Coasting through short gaps

03

Track validity

Before any semantic statement comes the question: physical target, or clutter and artefact? Moving vegetation, multipath propagation and processing artefacts all generate track-like structures. This stage is kept deliberately separate so clutter is not treated as an equivalent object class – and because it is structurally the most effective lever against false alarms.

Physical target track vs. artefact
Ghost-target handling
Its own measure in the false-alarm funnel

04

Semantic target assessment

A valid airborne track is assessed as drone, bird or other airborne object, or Unknown. The basis is micro-Doppler features, radar cross section fluctuation, and track and temporal features. Rotating propellers and beating wings do in principle produce different signatures; whether they separate robustly across sites is the central research question.

Micro-Doppler
RCS fluctuation
Track and temporal features

05

Unknown handling

Unknown is not a malfunction but a deliberate system decision. A classifier that forces a class on thin or contradictory evidence produces confidently wrong decisions – exactly what a control room cannot use. Where evidence is insufficient, the track is reported as uncertain rather than mislabelled.

Conservative uncertainty handling
No forced class
Defined degradation path

06

Planned optical verification

Target architecture: on a relevant candidate the system slews one of the customer's existing PTZ cameras, provided visibility, range and target geometry allow it. Radar and image information are combined into a conservative alert decision; the event then goes to the control room or VMS.

Radar-triggered PTZ cueing (R&D goal)
No continuous video recording intended
Handover to control room / VMS (R&D goal)

04 · Current development status

Aspect	Status
Permitted radar output	`possible moving drone candidate` or `Unknown`
Credible field tracking (small drones)	so far around 14–20 m
Sampling/evaluation boundary of the current waveform	about 30 m (a configuration choice, not a sensor limit)
Drone/bird/clutter in the field	development goal, not robustly demonstrated
PTZ cueing and VMS end-to-end	development goal, not demonstrated in the field
Weather and site robustness	to be validated
Active countermeasures	explicitly not part of the system

Sampling boundary, model forecast and real field result are three different numbers. The derivation is on the validation page.

05 · Which sites this is built for

Ozense develops first for a narrow profile: mid-sized civilian high-value sites with their own security or control-room organisation, existing or integrable PTZ/VMS technology, a concrete drone, espionage or disruption risk, and a spatially limited protection need – sites, in other words, for which premium systems are economically or organisationally oversized.

R&D, testing and prototype sites

Direct exposure to espionage and know-how loss, usually clearly delimited sensitive areas, and often private procurement.

Selected sensitive industrial facilities

High-value production and technical installations – initially as point or sector protection, not a full large-scale perimeter.

Selected data centres

Only where there is a drone-specific scenario, exposed plant, and an existing security organisation – not by industry label alone.

Deliberately not addressed first: airports and public authorities with high reference requirements, public spaces and municipalities, large unmanned sites without a response process, and consumer or residential applications.