MDD, the Miles Drone Detection software branded as Hemiseer, finds UAVs by using an array of antennas that passively watches the sky for reflected ambient RF signals. Both power and phase data are collected, processed through a unique metric that overlays both patterns.

Antennas are grouped together into clusters, typically 4 antennas along with local small computers to get baseband RF signals onto a network and to a system where they are combined.

A 3D location is sought as the source of an RF emitter, or reflector of ambient RF signals. Its energy and that of a common environmental background emitter reach all antennas. The phase, gain, and power of the emitter’s signal varies for each antenna due to distance and orientation of the antenna to the emitter location.

Each antenna provides an observation at each frequency and time index. These observations include power and phase of incoming RF energy. Each observation is a summation of all local emitters and a background environment energy. The more antennas available at any moment, the better the quality of observation.


Miles Space, Inc. 

FAQs

The detection problem becomes one of finding the emitter location, emitter signal, and environmental background signal most consistent with the observations. This is not classic bi-static radar correlation. At no point does MDD need to observe the true illumination signal. Rather, MDD makes estimates of the source of RF energy, whether those sources are active transmitters or passively reflecting ambient energy.

The MDD solver uses a special formula to assess the difference between the observations and a proposed environmental power, emitter location, emitter power level, and emitter phase at each frequency of interest. It totals this difference across many frequencies, each contributing to the final solution accuracy. A truthful hypothesis of emitter location, emitter signal, and environment signal results in a perfect score - a zero residual, meaning there is no difference between observation and the proposed solution.

Our solver scans the volume to be protected, solving for reflected energy at each point. Drones appear as local disruptions in the solver formula’s output. How “local” depends upon the frequency, antenna directivity, and sensor layout. For 4G, local ranges of 2-5 meters are typical. For 5G, ranges of 20-50 meters are projected, making for far fewer brief, but false, positive matches.

The system is smart about which frequencies it uses. Moment-by-moment, it monitors the spectrums of the contributing antennas, which themselves can vary moment by moment. Frequencies with high variation between antennas have the most inherent information and result in the best detections. Frequencies with low variation are discarded as they generate many false positives while consuming computing resources.

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