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Airports, stadiums, and critical infrastructure increasingly face unmanned aircraft where they do not belong. Detection is largely a solved problem; prediction is not. Confirmed, fully instrumented incursions are rare, the sensors that observe them disagree in frame, rate and latency, and the records that do exist are so internally repetitive that conventional evaluation overstates readiness.
Operators are not asking whether a drone is present. They are asking where the track will be in ninety seconds, whether it is converging on somewhere that matters, and how much of that answer they should trust – with a basis that will still hold up when a regulator asks why traffic was held.
ADDX VARDSKAR treats trajectory prediction as a data problem before a model problem. It manufactures the corpus the operational record never supplied, normalizes and governs that corpus so results are reproducible and auditable, and trains a physics-constrained prediction model on top. Real sensor feeds and generated streams enter the same service, and nothing reaches the model that has not passed the quality gates.
Data generation engine
Produces physically consistent truth trajectories, then emulates what each sensor would have reported given geometry, update rate, error, and latency. Coverage is designed rather than replayed, so the corpus contains the rare, high-consequence cases the operational record never captured.
Data transformation and normalization service
Harmonizes frames, aligns time, resolves identity and normalizes units, then applies quality gates and a leakage-control key that keeps near-duplicate tracks on one side of any evaluation split. Every published dataset carries a manifest, a version, and a route to roll it back.
ML Trajectory prediction model
Returns a predicted region with calibrated confidence and an intent classification, bounded by achievable flight dynamics for the inferred airframe class and evaluated on grouped splits rather than random ones.
Operators receive a predicted region with calibrated confidence instead of a point estimate, and an intent classification in which each category maps to a different response. Readiness is reported on leakage-controlled splits, so the figures reflect what the field will deliver rather than what a random split flatters.
Every prediction traces to a dataset manifest, a dataset version, and a model version. That audit trail is what separates a demonstrator from a system that survives its first serious incident, and it is built into the pipeline rather than reconstructed afterwards.
The architecture is not specific to unmanned aircraft. Maritime search and rescue drift, severe-weather cell tracking, wildfire spotting, and launch-anomaly debris estimation share the same three difficulties, and the same governance and evaluation discipline carries across unchanged.
ADDX VARDSKAR™ is offered as artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for creating complex data science analyses, and as software as a service (SAAS) services featuring software using artificial intelligence (AI) for use in creating machine learning in trajectory analysis for public safety.
All data described in this case study is open-source or synthetically generated.
