Each timestep, the tracker runs five steps — from prediction through lifecycle management.
Plain English
Every frame: predict where each drone probably moved based on its last known velocity → check whether each incoming signal is plausibly from a known drone or random noise → match signals to drones optimally (one signal per drone, one drone per signal) → update each drone's estimated position using the matched signal → manage new arrivals and drones that have gone quiet.
1
Predict
Where should each drone be now?
CV model, F·P·Fᵀ + Q
2
Gate
Is this signal close enough to be real?
Mahalanobis d² < χ²₉₉
3
Assign
Which signal belongs to which drone?
Hungarian O(n³)
4
Update
Correct the position estimate
EKF polar h(x)=[r,θ]
5
Manage
Handle new targets and lost ones
Birth / confirm / delete
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Fusing radar and camera
Radar reports distance and angle from the sensor. Camera reports pixel coordinates. These are measured in completely different units, with different noise characteristics. The filter converts both into a single best-estimate position — weighting each sensor by how trustworthy it currently is.
Technical: native polar measurement model h(x) = [√(x²+y²), atan2(y,x)] with analytic 2×4 Jacobian. Angle-normalised innovation prevents ±π wraparound. Camera uses a linear H after pixel→world scaling.
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Filtering out false alarms
Radar generates phantom signals constantly — birds, reflections, electrical noise. Before any matching happens, each incoming signal is tested: is it close enough to a known drone's predicted position, given how uncertain that prediction is? Signals that fail this test are discarded before they can corrupt any tracks.
Technical: Mahalanobis distance² in polar measurement space tested against χ²(2 DOF, 99%) gate before Hungarian assignment. Infeasible pairs receive sentinel cost 10⁹.
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Solving the assignment problem
When multiple drones are visible simultaneously, each sensor signal must be claimed by exactly one drone. A naïve approach — assign each signal to its nearest drone in sequence — can cascade errors when targets are close. Instead, the system finds the globally optimal assignment: minimize the total cost across all pairings at once.
Technical: scipy.optimize.linear_sum_assignment (Jonker-Volgenant, O(n³)). Order-independent global optimum vs. greedy nearest-neighbour.
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Managing track confidence
A single radar blip shouldn't be enough to declare a drone exists — it's probably clutter. A drone that stops producing signals for several frames probably left the area. The tracker requires two consecutive detections before confirming a track, and deletes it after three missed frames. This keeps the track list clean without human supervision.
Technical: TENTATIVE → CONFIRMED (hits ≥ 2) → DELETED (misses ≥ 3). Stale tentative tracks pruned after 3 frames.
The simpler version converts radar readings to x/y coordinates first, then filters. The advanced version works directly in radar's native units — more accurate at long range where the conversion introduces distortion.
