# The Ear Points the Eye: Acoustic Cueing: How Microphones Aim Cameras and Lidar at Drones

> A camera sees a drone sharply but through a straw. A microphone array hears the whole sky but vaguely. Put together, the ear points the eye, and the error falls from degrees to a fraction of one.

- Published: 13 November 2026
- Author: Ghulam Mujtaba, Ph.D. (https://drosonic.com/about.html)
- Publisher: DroSonic OSINT (https://osint.drosonic.com/), the research arm of DroSonic (https://drosonic.com/)
- Series: Sound and Sight, part 1 of 3
- Topics: Sensor Fusion, Acoustic Detection, Counter-UAS
- Confidence: moderate
- Web page: https://osint.drosonic.com/assessments/acoustic-cueing-cameras-lidar.html
- Cite as: Mujtaba, G. (2026, November 13). The Ear Points the Eye: Acoustic Cueing: How Microphones Aim Cameras and Lidar at Drones. DroSonic OSINT. https://osint.drosonic.com/assessments/acoustic-cueing-cameras-lidar.html
- Reuse: Text and original diagrams © DroSonic. You may quote this report and share its link with credit to DroSonic OSINT; to republish it in full, reuse our diagrams or use it commercially, contact us first. Photos and third-party figures keep the licences shown in their credits.

## Key questions

**What is slew-to-cue in drone detection?**
A wide-area sensor, such as a microphone array or radar, detects a possible drone and estimates its direction. That estimate 'cues' a narrow, high-resolution sensor, such as a telephoto camera or lidar on a gimbal, to 'slew' (turn) towards it, confirm what it is and track it precisely.

**Why pair microphones with cameras for drone detection?**
They fail in opposite ways. A telephoto camera that can resolve a small drone at 500 m sees under 1% of the sky. A microphone array hears in every direction, day or night, but its direction estimate is several degrees off. In one 2023 system, fusing the two (plus lidar) cut direction error from about 4.4° to 0.8° in azimuth and from 5.8° to 0.25° in elevation.

**How accurate is acoustic drone localisation?**
In a 2023 test of a 64-element microphone array against a drone orbiting at 180 m altitude, the acoustic direction error was about 4.4° in azimuth and 5.8° in elevation. That is enough to aim a camera with a 15° by 10° field of view, not enough on its own to guide an interceptor.

## Key numbers

- **~1%** of the sky seen by a drone-resolving camera (15° × 10° telephoto view)
- **4.4°→0.8°** direction error, ear alone vs ear plus eye (azimuth, one 2023 field test)
- **481 m** drone picked out of noise by the array (beamformed, 64 channels)
- **6 s** from switch-on to optics locked on (in the same test)

## Bottom line

**A camera good enough to identify a small drone at 500 metres sees about one per cent of the sky. A microphone array sees none of it but hears all of it.** That mismatch is why acoustic sensing and optics are increasingly built to work as a pair. The microphones find the drone's rough direction anywhere around the site; a gimbal swings the camera and a laser rangefinder (lidar) to that direction; the optics confirm the target and track it far more precisely than sound alone. In a 2023 field test of one such system, built at the Southern University of Science and Technology in Shenzhen, the acoustic estimate alone was off by about 4.4° in azimuth and 5.8° in elevation; with camera and lidar fused in, the errors fell to 0.8° and 0.25°, and the drone's position was known to within about 3 metres at 200 metres' range. The principle is simple and old. The design rule that follows from it is the important part: the ear only has to be accurate enough to land the target inside the eye's narrow window.

## What each sensor brings, and what it lacks

Each sensor in a slew-to-cue chain does one job well and one job badly. The pairing works because the gaps do not overlap.

| Sensor — what it is | What it does well | What it lacks |
|---|---|---|
| Microphone array — dozens of microphones on one board | Hears in every direction, day or night; emits nothing | Direction only to a few degrees; no range; confused by loud nearby sounds |
| Telephoto camera — narrow view on a gimbal | Sharp direction (under 1°); shows what the object is | Sees ~1% of the sky at a time; needs light; no range |
| Lidar — laser ranging on the same gimbal | Range to centimetres; works in poor light | Same narrow view as the camera; must be pointed |
| All three, fused | Wide search, sharp direction and range in one track | One gimbal follows one target at a time |
| Networked acoustic nodes — DroSonic's approach | Many small passive sensors classifying sound on board; coverage grows by adding nodes | A bearing, not an image: they cue the camera rather than replace it |

## Why sharp sensors need pointing

Every camera trades field of view against detail. An image sensor has a fixed number of pixels, so widening the view spreads them thinner and a small, distant drone shrinks to a handful of pixels. To see far, systems use telephoto lenses, which narrows the view further. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251)) The camera in the Shenzhen system covers 15° by 10° and puts more than 100 pixels on a 30-centimetre drone at 500 metres. Its lidar covers about 16° by 14° and measures distance to 2 centimetres within 500 metres. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

By our approximation, each of those windows is about 1% of the sky above the horizon. Scanning the whole sky with them would be like searching a stadium through a drinking straw. Spinning lidars do not solve it either: in one study cited by the authors, a 64-line automotive lidar picked up a small drone at up to 50 metres with only about three points, and missed it entirely when it flew between the scan lines. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

*Figure: Coverage against precision, the gap the pairing closes.* Diagram: a half-circle representing the sky above a sensor head on a gimbal. The whole half-circle is shaded as the microphone array's coverage, labelled heard anywhere here. A thin wedge pointing at a drone is the camera's view, labelled seen only here, with the lidar's view as a dashed wedge of almost the same size. Side notes: microphone array, whole upper hemisphere, direction error about 4–6°; camera, 15° by 10°, about 0.7% of the sky, error under 1°; lidar, 16° by 14°, about 1% of the sky, range to 2 cm within 500 m. The microphone array covers everything roughly; the optics cover a sliver precisely. Wedges drawn to scale.

## Hear it, then look at it

Cueing a narrow sensor from a wide one is how air defence has long worked; the Sound and Sight question is which sensor does the cueing. Before radar, acoustic locators and searchlights worked in pairs, as our report [Listening to the Sky](https://osint.drosonic.com/assessments/listening-to-the-sky.html) describes. For small drones the idea returned in research. In 2018 Embry-Riddle Aeronautical University described "Drone Net", a network of passive rooftop sensors meant as a cheaper alternative to radar at small airports: when its all-sky camera and acoustic network detect something moving, "it sends a message to begin electro-optical and infrared sensing by tilting and panning the camera", over an area of about a square kilometre. ([Unmanned Airspace](https://www.unmannedairspace.info/counter-uas-systems-and-policies/embry-riddle-researches-low-cost-drone-detection-system-small-airports/)) The team published the design at the AIAA SciTech Forum in January 2019. ([AIAA](https://doi.org/10.2514/6.2019-2264))

A French–German team took it to an extreme, pairing a four-microphone array with a short-wave infrared camera whose view was under 1.2° wide; without the acoustic estimate, that camera would have been close to useless. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

The same pattern is already on city streets. Since January 2021 Taiwan has used roadside "noise cameras" that photograph vehicles louder than 86 decibels on slower roads and 90 decibels on faster ones; the sound level decides when the camera fires. ([Taiwan News](https://www.taiwannews.com.tw/en/news/4067378)) The one in the photo above, in Taichung, is a microphone on a pole above a camera.

Commercial counter-drone systems mostly pair radar with electro-optical and thermal cameras. A 2024 review of the market found that "few incorporate acoustic sensors, or at least, their presence in the specifications is not clearly indicated", and that lidar is rarely included at all. ([Preprints.org](https://doi.org/10.20944/preprints202402.0551.v1))

## Microphones, a gimbal, a camera and a laser

The Shenzhen team's system is unusually well documented, which is why we use it here. Its array is a 40-centimetre square board carrying 64 elements of four microphones each, laid out in an irregular pattern chosen by a genetic algorithm to suppress false "side" directions. The board's size sets its angular resolution: about 8.7° for a 5 kHz tone. It listens over the whole hemisphere. A neural network trained on drone recordings mixed with wind, birds and insects cleans each frame of sound before the array estimates direction, raising the test signal-to-noise ratio from 6.5 to 11.7. In a recorded trial, a conventional beamformer was pulled off target by a nearby bird; the filtered one was not. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

When the array hears a drone, the gimbal turns the camera and lidar to the estimated direction. Once the target is in view, a tracking filter merges all three sensors' measurements into one track and keeps the gimbal on it. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

At the other end of the cost scale, a team at the National Technical University of Athens built a cross of four off-the-shelf microphones that estimates direction and runs a machine-learning check on whether the sound is a drone. In outdoor trials it detected drones reliably at more than 70 metres. ([Sensors](https://doi.org/10.3390/s22228659)) Even a cue that rough is enough to point a camera with a wide-enough lens.

## From degrees to a fraction of one

The team flew a drone carrying a high-precision satellite positioning receiver in an orbit 180 metres above an open field, with the system at the centre and its gimbal pointing straight up. The array found the drone and swung the gimbal; the optics locked on and began measuring six seconds after switch-on. Over the flight, the acoustic direction error averaged 4.4° in azimuth and 5.8° in elevation. Camera and lidar alone, once pointed, were each under 1°. Fusing all three gave the best result: 0.8° and 0.25°, and a position error of 2.8 metres at an average range of 206 metres. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

*Figure: The ear finds it, the eye pins it down, direction error by sensor.* Bar chart of direction error. Microphone array: 4.4° azimuth, 5.8° elevation. Camera: 0.9° and 0.3°. Lidar: 0.8° and 0.3°. All three fused: 0.8° and 0.25°, with a 3D error of 2.8 m at about 206 m. Sound alone is good enough to aim; optics make the track precise. Fusion adds a little more on top of the best single optic.

*Figure: Cue, slew, track, the flight in three panels.* Three plots. Left: a cone of acoustic direction estimates rising from the sensor towards ground-truth positions about 150 m up, with the gimbal still pointing vertically. Centre: the full flight path as a rectangle about 200 m up, with the fused track in red overlying the ground truth in blue. Right: azimuth and elevation against time over 100 seconds, with the fused estimates matching the ground truth closely after the moment t1 when the optics lock on. Left: the acoustic cone before the gimbal moves. Centre: the fused track on the true path. Right: after the optics lock on (t1), the estimate follows the truth.

Range is the other half of the case. Combining 64 channels lowers the background noise in proportion to the number of channels, which is what lets the array hear further than any single microphone. At 481 metres the drone was invisible in the spectrogram of one microphone and clear in the combined signal. Regulations capped the tests at 500 metres; by extending the measured trend, the authors estimate that the signal would sink below the background at about 1.3 kilometres. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251))

*Figure: 481 metres, one microphone hears noise, sixty-four hear a drone.* Two spectrograms of five seconds of sound with frequency up to 10 kHz. Left, one microphone at 481 m: an even wash of noise with no visible pattern. Right, the 64-channel steered signal: a darker background with a clear repeating arch-shaped drone sound pattern between about 2 and 6 kHz, marked with an arrow. The same moment recorded by a single channel (left) and by the whole array steered at the drone (right).

> The ear does not need to be precise. *It needs to be precise enough to land the target inside the eye's window.*

## Match the cue to the window

Put the numbers side by side and a practical rule appears. This is our analysis, not the authors'. The camera's view is 15° wide and 10° tall, so a target is in frame if the cue is within about 7.5° left or right and 5° up or down. The array's average azimuth error, 4.4°, sits comfortably inside the first; its 5.8° elevation error is at the edge of the second. In this system, then, the cue usually lands in frame across, but sometimes needs a short search up or down. A sensor whose view is ten times narrower, like the infrared camera in the French–German study, needs a cue ten times better, or a search pattern. This is the rule DroSonic designs its acoustic cue around: a bearing good enough to put a camera, or a crew's eyes, on the right patch of sky. ([DroSonic](https://drosonic.com/#technology))

The study's limits are equally clear. It flew one drone, at one site, against a clear sky, where the authors note that simple image processing was enough to pick out the target; cluttered backgrounds, birds and night would be harder for the camera. Its array can follow several drones at once, but one gimbal can only watch one; the authors suggest more gimbals or faster slewing. ([Advanced Intelligent Systems](https://doi.org/10.1002/aisy.202300251)) A swarm is the case where a wide-area acoustic layer matters most and a single optical turret matters least.

For a buyer or integrator, the questions follow:

1. **How large is the acoustic direction error, in azimuth and elevation, at the ranges that matter?** Compare it with half the optic's field of view.
2. **How long from first sound to optics on target?** Six seconds is one test; ask for the distribution.
3. **How many targets can the optics follow at once?** The ear can hear several; one turret can look at one.
4. **What happens when the optic cannot confirm?** At night, in cloud or against clutter, does the acoustic track still reach the operator?
5. **Was it tested against noise, not just at range?** Birds, traffic and wind are where acoustic cues go wrong.

### Confidence: How sure we are, and what would change it

**High confidence** in the principle: wide-area acoustic sensing can cue narrow optics, and fusing them gives a more precise track than either alone. Independent research groups, a university airport project and civic noise enforcement all use the pattern.

**Moderate confidence** in the specific figures. They come mainly from one peer-reviewed field study with one drone, one site and clear skies, with ranges capped at 500 metres; the 1.3-kilometre figure is the authors' extrapolation, not a measurement. The design rule in Field 05 is our reading of their numbers.

**What would change it:** published trials of acoustic cueing against several drones at once, at night and in cluttered or noisy environments, and data from fielded counter-drone systems, which rarely publish their acoustic performance.

## Built to cue, not to replace

DroSonic builds the ear in this pairing: networked passive acoustic sensors that detect, classify and locate drones by the sound of their propellers, running neural networks on low-cost edge hardware and emitting nothing. ([DroSonic](https://drosonic.com/#technology)) Its U.S. provisional patent application, filed in September 2026, covers detection, classification, localisation and tiered defeat cueing, the step of handing a track to whatever looks or acts next. ([DroSonic](https://drosonic.com/news/patent-filed.html))

That is the division of labour this report describes. The acoustic layer does not need a camera's precision, only enough to point one. A DroSonic prototype was tested against live drones at the DIRT Days 26.04 field exercise in September 2026; DroSonic reports that it heard drones other sensors missed. ([DroSonic](https://drosonic.com/news/dirt-days-field-test.html)) No performance figures from that test have been published, so we quote none here.

## The ear is the first layer

Acoustic sensing is often compared with cameras and radar as if they were rivals. The evidence here points the other way: a microphone array is the natural wide-area trigger for sensors that are precise but blind outside a narrow window. It covers the whole sky, works day and night and emits nothing, and all it has to do is be right to within half the width of the camera's view. Ukraine's national acoustic network is the large-scale version of the same logic, cueing mobile fire teams rather than turrets. ([Defense One](https://www.defenseone.com/defense-systems/2024/07/ukraines-cheap-sensors-are-helping-troops-fight-waves-russian-drones/398204/))

[DroSonic](https://drosonic.com/) builds passive acoustic detection to play exactly that role: hear the drone first and hand that cue to whatever sensor or team acts next. Part 2 of Sound and Sight looks at the next step: when several sensors report at once, how their evidence is actually combined, and what each one adds when the others fail.

## Related reports

- [What a Microphone Can Hear: Acoustic Drone Detection Explained](https://osint.drosonic.com/assessments/acoustic-drone-detection-explained.html): Acoustic sensors detect drones by the sound they can't switch off. How they work, how far they hear, and where they beat radar and RF.
- [Listening to the Sky: Acoustic Drone Detection: WW1 to Sky Fortress](https://osint.drosonic.com/assessments/listening-to-the-sky.html): Ukraine rebuilt a century-old idea, listening for aircraft, with thousands of cheap microphones. How it works, where it fails, and how the U.S. could use it.
- [Hearing the Drop: Prison Drone Drops: Detecting the Delivery by Sound](https://osint.drosonic.com/assessments/prison-drone-drops-acoustic-detection.html): Drones now deliver drugs, phones and weapons into prisons thousands of times a year. New research shows a microphone can tell the moment the package falls.