SolvFilter — Scalable Multi-Target EKF
140-state Riccati EKF prediction at fleet scale — 74× cheaper than UKF with best NEES consistency. The tracking step the counter-UAV kill chain depends on.
- 140/140
- Riccati survival @ dim=140
- 74×
- Cost vs UKF at dim=140
- 0/280
- scipy baseline survival
The scenario
Set the picture
A multi-target tracker must propagate dozens of coupled target states and full covariance matrices every radar update — under noise, at 10 Hz, on embedded hardware. When the prediction integrator fails or blows up NEES consistency, the track picture lies and the kill chain breaks upstream.
UKF is the usual answer at moderate dimension. At dim=140 it becomes the cost and consistency bottleneck.
What it costs today
UKF at dim=140: accurate but expensive — unsustainable at fleet concurrency on embedded processors.
Standard scipy EKF predict: 0% survival on the SF-3 Riccati slate at dim=140 under the validated noise model.
What changes with SolvNum
SolvFilter is a dedicated EKF prediction engine — not a general ODE solver — optimized for multi-target tracking at scale.
SolvFilter propagates state and covariance with NEES-consistent statistics at dim=140 where UKF cost dominates. Built on the same noise-survival foundation as SolvSRK for the underlying integration when coupled dynamics are required.
Measurable outcome
What we'll claim — and how it survives review
Each line below maps to a captured number in the demo section. Every number is reproducible from the SolvNum validation suite.
- SF-3 CONFIRMED: 140/140 Riccati cells survived at dim=140 (20 seeds) vs 0/280 for scipy baselines.
- 74× lower computational cost than UKF at dim=140 on the validated benchmark.
- Best NEES consistency among compared filters at scale.
- SF-4 CLOSED: fast-path + dim=140 approved; IEKF stiff variant parked.
- Operational multi-target field tapes: POC hardened — partner replay (G-036).
The demo
What was tested. How. What the script printed.
SF-3 validation slate: continuous Riccati EKF at dim=140, 20 seeds, scipy UKF and RK45/Radau baselines for comparison. Measured survival, NEES, and cost ratio.
Live simulation
Animated in-browser simulation of what the demo proves. The numbers underneath are the captured demo output.
Kill chain cycle 1
25ms SolvSRKRadar signal ODE integration (FMCW beat-frequency, pulsed Doppler I/Q)
Full counter-UAV kill chain: detect → track → identify → prioritize → assign → engage → assess. SolvSRK powers the ODE integration at every physics-intensive step. Times shown are per-drone wall-clock from the validated SolvSRK defense-autonomy envelope. The entire cycle completes in under 1.1s per threat — fast enough to close the loop before the swarm’s next maneuver window.
Composes with
Where this POC sits in the substrate
Every POC reinforces — and is reinforced by — others. Click through to see how each piece locks into the larger picture.
Evidence pointers
Where the claims live in the repo
These are the files a reviewer should run, read, or grep to re-derive every number on this page.
- solv/products/solvfilter/overview.md
- solv/17-defense-validation-gaps.md (G-036)
- SF-3 triage CONFIRMED 2026-05-30
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ITAR-aware. Air-gapped delivery available. Every claim above traces back to a script in the public repo.