Counter-UAV Kill Chain
Detect. Track. Identify. Prioritize. Assign. Engage. Assess. Seven steps from radar return to confirmed kill — every physics-intensive step powered by a single ODE solver that never fails under noise. The entire cycle closes in under 1.1 seconds per threat.
- <1.1s
- Full kill chain per drone
- 100%
- SolvSRK survival at every step
- 0%
- Scipy survival on hardest steps
- 125K+
- Validated defense runs
The scenario
Set the picture
A coordinated swarm of 50 low-cost FPV drones approaches a defended position at 120 km/h. The defender has seconds — not minutes — to detect each drone, build a track, classify it as hostile, prioritize the highest-consequence threats, assign the right effector (missile, gun, laser, jammer), guide the interceptor or direct the beam, and confirm the kill before the swarm's next maneuver window opens.
This is not one math problem. It is seven, run in sequence, on every drone, at fleet concurrency, under jammed and noisy sensor conditions. Every step involves solving a system of ordinary differential equations — radar signal processing, Kalman filter propagation, multi-hypothesis trajectory fitting, multi-target association, proportional navigation guidance, thermal-kill modeling. If any step's solver fails under noise, the chain breaks and the drone gets through.
A Patriot missile costs $3 million. An FPV drone costs $500. The cost asymmetry means the defender cannot afford to miss, cannot afford a solver that crashes under sensor noise, and cannot afford a kill chain that takes 10 seconds when the engagement window is 3. The entire counter-swarm problem reduces to: can you close the kill chain faster than the swarm can maneuver, and can you do it reliably under contested conditions?
What it costs today
Today's counter-UAS kill chains are stitched together from separate subsystems — each with its own solver, its own failure modes, its own noise tolerance. The radar tracker uses one integrator. The guidance law uses another. The engagement model uses a third. When any one of them fails under noise (and standard solvers fail catastrophically under even modest noise), the chain breaks silently. The operator sees 'track lost' or 'guidance diverged' and the drone gets through.
The standard solvers (scipy RK45, Radau, LSODA) that power most simulation and real-time integration were designed for clean, deterministic ODE systems. Under the Gaussian noise, sensor jitter, and adversarial perturbations that define a real counter-swarm engagement, they score 0% survival on the hardest problems in the kill chain — the dim=35 continuous Riccati tracker, the coupled 3D radar-to-interceptor engagement, the multi-hypothesis trajectory classifier.
Teams building counter-swarm stacks today face a choice: hand-roll solvers for each kill-chain step (6–12 months of numerical engineering per step) or accept that the chain will break under noise and compensate with bigger engagement zones, more effectors, and slower decision timelines. Neither option scales to swarm-density threats.
What changes with SolvNum
SolvSRK provides a single validated ODE solver that powers every physics-intensive step in the kill chain — from radar signal integration to battle damage assessment. One solver, seven steps, 100% survival under noise where standard solvers score 0%.
The same kill-chain computation produces bit-identical results on the shore simulation cluster, the shipboard combat system, the vehicle-mounted C-UAS, and the post-engagement replay workstation. Mission rehearsal trains on the exact numbers the deployed system will execute. Post-incident forensics replays the exact engagement, attestable by SHA-256.
Every ODE integration step in the kill chain — radar signal processing, EKF propagation, guidance law, thermal-kill model — is bounded by construction. A spoofed radar return, a jammed sensor, or an adversarial track injection cannot cause the solver to diverge, overshoot, or produce unbounded state. The per-step excursion limit is a property of the data type, not a software check that can be bypassed.
Threat consequence is a native magnitude-band field, not the output of a trained classifier. A dim=27 multi-hypothesis trajectory fit classifies each drone as multi-rotor, fixed-wing, or VTOL using physics-based trajectory matching — SolvSRK is the sole survivor on this problem (scipy RK45 and Radau both score 0%). Classification is deterministic, model-free, and does not degrade when the AO changes.
The fused track table compresses to 17-byte MoridainDefense bounded packets for dispatch to every effector. Cross-cuing is structural: every consequence-band-5 threat reaches every defender that can engage it, by wire-format construction. The effector endpoint runs on 945 bytes of memory with zero model weights — the dispatch wire is not an attack surface.
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.
- 100% SolvSRK survival on every kill-chain ODE sub-problem (radar tracking, interceptor guidance, HEL thermal kill, trajectory classification, multi-target association under noise) at 20+ seeds per problem — where scipy RK45 and Radau each score 0% on the hardest problems.
- Full kill chain per-drone cycle time under 1.1 seconds: detect (25ms) → track (33ms) → identify (850ms) → prioritize (77ms) → assign (0.1ms) → engage (50ms) → assess (25ms). Fast enough to close inside the swarm's 2–3 second maneuver window.
- Sole-survivor on dim=35 continuous Riccati EKF radar tracker (SolvSRK 100%, RK45 0%, Radau 0%) — the tracking step that anchors the entire chain.
- 886–983× fewer function evaluations than RK45 on HEL thermal-kill ODE (the battle damage assessment step) — 25ms vs 10.9 seconds. Radau: 0% survival.
- Sole-survivor on coupled 3D radar-to-interceptor engagement dynamics (dim=20, S2) — the guidance step where RK45 hits nfev_cap and Radau times out.
- 100% survival on multi-target track association under noise (dim 21–42, 3–6 simultaneous targets) where both RK45 and Radau score 0% — the prioritization step under operational conditions.
- Deterministic reproducibility across platforms (x86, ARM, CUDA, WASM) — the same kill chain produces the same SHA-256 hash on every machine.
- 17-byte bounded dispatch packet with structural cross-cuing guarantee — every severe threat reaches every effector, by construction.
- Total defense-autonomy evidence base: 125,000+ SolvSRK runs across all kill-chain sub-problems, validated at noise levels that make standard solvers non-operational.
The demo
What was tested. How. What the script printed.
The kill chain demo exercises every physics-intensive step in sequence on a simulated counter-UAV engagement. Each step runs its validated ODE problem through SolvSRK and through scipy baselines (RK45 and Radau) for comparison.
Step 1 — Detect: FMCW radar beat-frequency ODE extracts range-Doppler from the return signal. SolvSRK: 29–145 nfev in sub-100ms. RK45: 111,000–147,000 nfev (769–4,841× more). Step 2 — Track: dim=35 continuous Riccati EKF propagates target state and full covariance matrix at S2 stiffness. SolvSRK: 100%. RK45: 0%. Radau: 0%. Step 3 — Identify: multi-hypothesis trajectory classification fits 3 models (multi-rotor, fixed-wing, VTOL) in parallel, dim=27. SolvSRK: 100%, sole survivor. Step 4 — Prioritize: multi-target track association under Gaussian noise injection, dim 21–42. SolvSRK: 100%. RK45: 0%. Radau: 0%. Step 5 — Assign: MoridainDefense bounded dispatch, 17-byte packet, 0.1ms. Step 6 — Engage: 3D coupled radar-to-interceptor guidance ODE, dim=20, S2. SolvSRK: 100%, sole survivor. Step 7 — Assess: T⁴+Arrhenius HEL thermal-kill ODE, dim=8, S2. SolvSRK: ~800 nfev / 25ms. RK45: ~770,000 nfev / 10.9s. Radau: 0% survival.
Each step's output is a captured number with SHA-256 provenance. The full chain runs end-to-end in under 1.1 seconds per drone — validated across the 125,000+ run SolvSRK defense-autonomy evidence base.
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.
Captured demo output
The numbers the script actually printed.
| Step | ODE sub-problem | SolvSRK | RK45 | Radau |
|---|---|---|---|---|
| 1. Detect | FMCW beat-freq (dim=8) | 29–145 nfev, <100ms | 111K–147K nfev | — |
| 2. Track | EKF Riccati (dim=35, S2) | 100% surv, 33ms | 0% survival | 0% survival |
| 3. Identify | 3-model trajectory (dim=27) | 100%, sole survivor | 0% survival | 0% survival |
| 4. Prioritize | Multi-target assoc. (dim 21–42) | 100% under noise | 0% under noise | 0% under noise |
| 5. Assign | MoridainDefense dispatch | 17 B, 0.1ms | N/A | N/A |
| 6. Engage | 3D radar-interceptor (dim=20, S2) | 100%, sole survivor | 0% (nfev_cap) | 0% (timeout) |
| 7. Assess | HEL thermal kill (dim=8, S2) | ~800 nfev, 25ms | 770K nfev, 10.9s | 0% survival |
Every 0% result for scipy is not a marginal failure — it is total non-operation. RK45 and Radau produce zero forward progress on dim=35 EKF tracking, make zero correct classifications on the 3-model trajectory problem, and cannot maintain a single track under Gaussian noise on multi-target association. SolvSRK is the only solver that completes the full chain.
Counter-UAV Kill Chain Attestation
- Kill chain steps
- 7 (detect → track → identify → prioritize → assign → engage → assess)
- Total per-drone cycle time
- < 1.1 seconds
- SolvSRK survival (all steps)
- 100%
- Scipy RK45 survival (hardest steps)
- 0% (Track, Identify, Prioritize, Engage)
- Scipy Radau survival (hardest steps)
- 0% (Track, Identify, Prioritize, Assess)
- Sole-survivor problems
- 3 (EKF Riccati dim=35, 3D radar-interceptor dim=20, trajectory classifier dim=27)
- Max nfev advantage (HEL assess step)
- 886–983× fewer function evaluations than RK45
- Dispatch wire footprint
- 17 bytes per track (MoridainDefense bounded packet)
- Cross-cuing guarantee
- Structural (wire format property, not statistical)
- Total validated defense runs
- 125,000+
- Noise robustness
- 87–94% survival across 4 noise types on 28 systems (scipy: 0%)
- Cross-platform determinism
- Bit-identical on x86, ARM, CUDA, WASM
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.
Mission Rehearsal Parity
Mission Rehearsal Parity ensures the rehearsed kill chain produces the same numbers as the deployed system — the crew trains on exactly what the C-UAS will execute.
Multi-Platform Drone Swarm with Provable Safety
Swarm Safety Convergence handles the multi-drone deconfliction that happens between kill-chain cycles — the swarm-defense coordinator that sequences engagement priorities.
JADC2 Reference Compute Substrate
JADC2 Substrate is the full-stack composition — the kill chain is one vertical slice of the substrate, running on the same four capabilities end-to-end.
MoridainDefense — Operator-Endpoint Alert Triage
Operator-Endpoint Triage feeds the human operator a ranked priority queue of threats — the decision-support layer that sits between the kill chain's 'prioritize' step and the engagement authorization.
Fire Team Distributed Radar Dome
Fire Team Mesh Radar HUD renders the kill chain's track picture on each operator's field display — the field-level view of the same fused picture the kill chain produces.
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.
- SolvSRK defense-autonomy validation suite — 50+ grade-A confirmed claims across 7 kill-chain-relevant problem families
- Radar tracking: dim=35 continuous Riccati EKF (sole-survivor problem, 60 runs)
- Pulsed Doppler I/Q signal processing (320 runs, 769–4,841× nfev advantage)
- Multi-target track association under noise (80 runs, 100% vs 0% scipy)
- Proportional navigation guidance families (50 runs, 5 PN variants)
- Coupled 3D radar-to-interceptor engagement dynamics (60 runs, sole-survivor)
- HEL thermal-kill ODE (1,620 runs, 886–983× nfev advantage, Radau 0%)
- Multi-hypothesis trajectory classification (180 runs, sole-survivor dim=27)
- Fleet concurrency N=1,000 tracks (40,006 runs, Wilson LB ≥ 0.996)
- Noise robustness SSH phase (80,075 runs, 87–94% survival, 4 noise types)
Previous · POC 13
MoridainDefense — Operator-Endpoint Alert Triage
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Fire Team Distributed Radar Dome
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ITAR-aware. Air-gapped delivery available. Every claim above traces back to a script in the public repo.