Grow the signal.Detect the anomaly.
UrbanFlux helps detect non-cooperative drones by combining publicly available signals and privacy-preserving aggregate data into a shared, explainable picture, so anomalies can be corroborated before they become operational surprises.
Designed for situational awareness without identity-based surveillance or storage of personal imagery.
Four phases. Each closes on a measurable criterion.
A twelve-month programme taking the system from a simulation-validated prototype to capability proven in the field. Every phase ends on a criterion that can be checked independently.
TRL 6 to TRL 7
- 01Months 1 to 3
Live sensor integration and data quality
Closes whenTwo or more heterogeneous live feeds sustain continuous ingest and survive the loss of any one source. Signal-to-noise and end-to-end latency are characterised against a threshold agreed with the technical authority.
- 02Months 4 to 6
Detection trials
Closes whenDetection rate, false-alarm rate and confidence calibration are measured against two drone types with distinct flight signatures. Passive RF sensing is brought online against live signals.
- 03Months 7 to 9
Edge deployment and interoperability
Closes whenAn edge node runs in the field, emitting CoT and TAK to a partner command system, with behaviour tested under degraded conditions. A privacy impact assessment confirms the pipeline retains anonymised aggregates only.
- 04Months 10 to 12
Generalisation
Closes whenThe same core, unmodified, extends to a second threat class and a second environment through connectors alone.