# Source Registry — SQ1 — Existing / competitor GPS-denied UAV navigation systems

> Mode A Phase 2 — engine Step 2 (Source Tiering & Exhaustive Web Investigation).
> Critical-novelty sensitivity per Step 0.5 in `../00_question_decomposition.md`. Time windows applied:
> - **Lead-candidate / SOTA claims**: prefer sources within last 6 months; up to 18 months if older is the official authority.
> - **Library/SDK API behaviour**: must reflect the currently shipped version at search time (`context7` mandatory per lead candidate).
> - **Established baselines** (KLT, RANSAC, EKF, ORB, SIFT, GTSAM): no time window.
>
> This file replaces a section of the previous monolithic `01_source_registry.md`. See `00_summary.md` for the full category index. Investigation order is tracked in `../00_question_decomposition.md` and the cross-category Investigation Status table in `00_summary.md`.

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### Source #25
- **Title**: Twist Robotics develops OSCAR — a GPS-independent visual navigation system for drones resistant to electronic warfare equipment
- **Link**: https://www.pravda.com.ua/eng/news/2026/01/28/8018266/
- **Tier**: L2 (national newspaper of record reporting on a Technology Forces of Ukraine release; primary press is the Technology Forces of Ukraine FB post)
- **Publication Date**: 2026-01-28 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window)
- **Target Audience**: Ukraine-deployment practitioners; UAV companion-system designers
- **Research Boundary Match**: **Full match** — Ukrainian fixed-wing-class UAV, GPS-denied, vision-based, deployed in active conflict
- **Summary**: Twist Robotics (UA) deployed OSCAR ("Optical System of Coordinates with Automatic Relocalisation") — camera + landmark-matching + map → autopilot ingests as a "reliable GPS signal". Vendor claims: 20 m accuracy without cumulative error, day/night/fog operation, 500,000 km logged across 25,000 combat missions over 24 months development, AI-augmented + Obrii proprietary simulator for training. Note: hardware photo shows active cooling on the module — implies non-trivial compute (probably Jetson-class). **No public independent benchmark.** Closest deployed peer system to this project.
- **Related Sub-question**: SQ1 (closest peer); also informs SQ8 (anti-spoofing claims), SQ9 (synthesis)


### Source #26
- **Title**: Ukraine Gives Drones Vision-Based Navigation to Push Past Heavy Jamming — The Defense Post
- **Link**: https://thedefensepost.com/2026/01/29/ukraine-drones-vision-navigation/
- **Tier**: L2 (defense-trade publication; corroborates Source #25 with a second-party byline)
- **Publication Date**: 2026-01-29 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense-policy / procurement readership
- **Research Boundary Match**: Full match
- **Summary**: Confirms OSCAR is operational, terrain-imagery-against-mapped-landmarks pattern, autopilot-ingestion. Adds "live imagery" framing. No new technical detail beyond Source #25.
- **Related Sub-question**: SQ1


### Source #27
- **Title**: Ukraine's Ruta Missile Drone Will Get an EW-Immune Navigation System — Defense Express
- **Link**: https://en.defence-ua.com/weapon_and_tech/ukraines_ruta_missile_drone_will_get_an_ew_immune_navigation_system-14541.html
- **Tier**: L2 (defense-trade publication, Ukraine-domestic)
- **Publication Date**: 2025-05-17 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 18-month authority window)
- **Target Audience**: Defense-procurement / industry analysts
- **Research Boundary Match**: Partial — operational profile (cruise-missile-class, terminal guidance) differs from our 8-h fixed-wing surveillance/strike profile; technique class is closely related (DSMAC pattern)
- **Summary**: Destinus Ruta (Ukrainian-Swiss origin; ~300 km strike range, miniature cruise missile) will integrate a navigation system from UAV Navigation (Spanish, Grupo Oesía). Defense Express infers DSMAC-style operating principle: "takes images of surface mid-flight, identifies location through comparison with reference". Vendor announcement notes validation in Ukrainian combat conditions including GNSS-denied / jamming / spoofing. Establishes that the cruise-missile-tier vision-nav pattern is now being miniaturised for ~300 km strike drones.
- **Related Sub-question**: SQ1 (commercial/military landscape)


### Source #28
- **Title**: Kilometer-Scale GNSS-Denied UAV Navigation via Heightmap Gradients: A Winning System from the SPRIN-D Challenge
- **Link**: https://arxiv.org/abs/2510.01348
- **Tier**: L1 (peer-style preprint, full system description, real flight data, competition results)
- **Publication Date**: October 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: arXiv v1 (2510.01348v1)
- **Target Audience**: GNSS-denied UAV system designers (academic + practitioner)
- **Research Boundary Match**: **Partial — different regime.** Multirotor (≤25 kg), <25 m AGL, LiDAR-equipped, no satellite-tile basemap; 9 km waypoint mission. Our project is fixed-wing, ~1 km AGL, no LiDAR, monocular + sat-tile basemap. **Architectural pattern transfers; specific algorithm does NOT** (heightmap gradients require LiDAR).
- **Summary**: CTU Prague team won SPRIN-D Funke Fully Autonomous Flight Challenge with: VIO (OpenVINS) + LiDAR-derived local heightmap + gradient template matching against open-data DEM + clustered K-means particle filter, all on Intel NUC i7 16 GB CPU-only (no GPU). Achieved RMSE <11 m over kilometer-scale flights vs ≤53 m for raw odometry. Critical observations explicitly stated:
  - **RTAB-Map and ORB-SLAM3 both fail** beyond 1 km / above 2 m/s flight (compute/memory) and ORB-SLAM3 loses tracking in textureless areas — directly applicable to our 17 m/s cruise over agricultural steppe.
  - **"Some teams used RGB satellite image-based matching, but this has proved to be highly unreliable at such low altitudes."** This is a low-altitude (<25 m AGL) finding; our 1 km AGL operates in the high-altitude regime where the same paper notes RGB sat-matching "works reasonably well" (refs [5][6]).
  - Lesson: "ability to recover from periods of high uncertainty and re-localize is more critical than maintaining consistently low instantaneous RMSE." Direct architectural input for AC-NEW-2 / AC-NEW-8.
  - Lesson: IMU-from-airframe vibration isolation is mission-critical for VIO usability.
  - Lesson: magnetometer is unreliable near steel-reinforced structures; sensor-fusion is essential for heading robustness.
- **Related Sub-question**: SQ1 + SQ5 (failure modes for VIO/SLAM at speed) + SQ2 (canonical pipeline)


### Source #29
- **Title**: Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints
- **Link**: https://arxiv.org/abs/2506.09748 (PDF: https://arxiv.org/pdf/2506.09748)
- **Tier**: L1 (peer-submitted preprint, IEEE-bound, with public CS-UAV dataset)
- **Publication Date**: June 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (within 6-month critical-novelty window for SOTA claims)
- **Version Info**: arXiv v1 (2506.09748v1)
- **Target Audience**: Academic SOTA researchers + UAV-localization implementers
- **Research Boundary Match**: **Full match** — exact same problem (UAV absolute visual localization in GNSS-denied conditions, downward-facing camera, satellite reference)
- **Summary**: 2025 SOTA pipeline: (1) image retrieval module (off-the-shelf, optimal-transport feature aggregation), (2) Semantic-Aware and Structure-Constrained Matching Module using **DINOv2** features + 4D correlation tensor + SoftMNN + 4D conv, (3) lightweight fine-grained module for pixel-level. Constructs UAV absolute visual-loc pipeline **without VIO/relative-loc dependence** (retrieval-and-matching only). Evaluation on AerialVL + their own CS-UAV. **Direct relevance**: this is a candidate template for our C2 (VPR) + C3 (cross-domain registration) components, but DINOv2 is a heavyweight foundation model — must be benchmarked under our 25 W / 8 GB Jetson Orin Nano envelope before selection (handed off to SQ3/SQ4 + SQ5 for that component).
- **Related Sub-question**: SQ1 (academic SOTA), SQ3+SQ4 (C2/C3 candidates), SQ5 (Jetson-on-Foundation-Model failure mode)


### Source #30
- **Title**: Raptor — GPS-Denied UAV Navigation & Coordinate Extraction (Vantor product page; Guide / Sync / Ace suite)
- **Link**: https://www.vantor.com/product/mission-solutions/raptor/
- **Tier**: L2 (vendor product spec; primary for the product itself, not for independent benchmark numbers)
- **Publication Date**: live (accessed 2026-05-07; references Mar 2026 + Dec 2025 + Sep 2025 partner blog posts indicating active product line)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense / commercial / industrial UAV integrators
- **Research Boundary Match**: **Full match** — vision-based aerial position software using existing camera + 3D terrain data, deployable on commodity hardware
- **Summary**: Vantor Raptor product family: **Guide** (on-drone vision-based positioning, demonstrated <7 m absolute accuracy in all dimensions, day/night/low-altitude, runs on commodity HW); **Sync** (georegisters live drone video against 3D terrain in real time, <3 m coordinate extraction); **Ace** (laptop-side coordinate extraction at <3 m). Backbone: Vantor's "100 million-plus sq km of highly accurate 3D terrain data, regularly updated" (Vivid Terrain, 3 m accuracy). Inertial Labs partnership (VINS-integrated Raptor Guide). Use cases include joint multi-domain ops, large-scale autonomous delivery, search-and-rescue. **This is the closest production-grade commercial peer to the project's architecture (sat-basemap-as-service + on-drone vision).**
- **Related Sub-question**: SQ1 (commercial), SQ3+SQ4 (commercial alternatives to building C2/C3 ourselves), SQ8 (basemap as a service vs offline cache)


### Source #31
- **Title**: Auterion successfully completes Artemis program to deliver long-range deep strike drone (press release)
- **Link**: https://auterion.com/auterion-successfully-completes-artemis-program-to-deliver-long-range-deep-strike-drone/
- **Tier**: L1 (official vendor press release)
- **Publication Date**: 2025-10-15 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Defense-procurement; UAV-integration architects
- **Research Boundary Match**: **Full match** — fixed-wing-class one-way attack drone with Ukraine-validated GPS-denied navigation; the system architecture is directly comparable
- **Summary**: Auterion Artemis (DIU project, completed Oct 2025) = Shahed-style design developed in Ukraine; up to 1,000-mile range; up to 40 kg warhead; runs on Auterion Skynode N mission computer + Auterion Visual Navigation system + built-in terminal guidance. Government evaluators signed off after operational flight tests in Ukraine including ground launch, GPS and GPS-denied navigation, long-range transit, and terminal engagement. **Establishes that the integration pattern (companion-class autopilot + visual navigation + terminal guidance) is shipping at production scale to a US defense customer.** Open architecture, manufacturing in US/UA/DE.
- **Related Sub-question**: SQ1


### Source #32
- **Title**: Bring AI and computer vision to small autonomous systems — Auterion Skynode S product page
- **Link**: https://auterion.com/product/skynode-s
- **Tier**: L2 (vendor product spec)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Small-UAS integrators
- **Research Boundary Match**: Full match (companion-class autopilot with NPU)
- **Summary**: Auterion Skynode S = compact mission computer with **dedicated Neural Processing Unit** for AI / computer-vision applications on small UAS systems. Architecturally the same niche our Jetson Orin Nano Super sits in (companion compute + autopilot integration), but with Auterion's PX4 fork pre-integrated. Hardware/runtime envelope is comparable; the product establishes that this is a product category, not a one-off integration.
- **Related Sub-question**: SQ1, SQ7 (alternate companion HW for adjacent context)


### Source #33
- **Title**: snktshrma/ngps_flight — Next-Generation Positioning System for ArduPilot (GSoC 2024)
- **Link**: https://github.com/snktshrma/ngps_flight (sibling: https://github.com/snktshrma/ap_nongps)
- **Tier**: L1 (open-source code repository, published GSoC project under ArduPilot organisation)
- **Publication Date**: GSoC 2024 timeframe (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Version Info**: GSoC 2024 prototype (research-grade, not production firmware)
- **Target Audience**: ArduPilot integrators building visual-positioning companion stacks
- **Research Boundary Match**: **Full match — closest open-source peer to our exact pipeline.** ArduPilot, downward-facing camera, satellite-image reference, deep-learning matching, fused with VIO, fed back to autopilot.
- **Summary**: NGPS = ROS 2 + ArduPilot pipeline composed of three packages: **`ap_ngps_ros2`** (visual geo-localization at 1–2 Hz by matching live camera frames to georeferenced satellite imagery using **LightGlue + SuperPoint**); **`ap_ukf`** (Unscented Kalman Filter fusing NGPS absolute positions with VIO estimates); **`ap_vips`** (VIO providing relative pose). Output is fused odometry to ArduPilot's EKF via `VISION_POSITION_ESTIMATE` (per the related issue #23471 framing). **This is the architectural template** the project should explicitly compare against — same component split as our C1+C2+C3+C5+C8 stack.
  - Caveats: (a) GSoC prototype, not production-hardened; (b) uses `VISION_POSITION_ESTIMATE` which on AP requires EKF source set 2/3 with EK3_SRC*_POSXY=Vision; our SQ6 conclusion picked `GPS_INPUT` as primary AP path because it carries `horiz_accuracy` directly and supports source-set switching via `MAV_CMD_SET_EKF_SOURCE_SET` — must compare the trade-off in design phase; (c) no documented spoofing-defence integration; (d) no documented covariance-honesty contract.
- **Related Sub-question**: SQ1 (closest open-source peer), SQ2 (canonical-pipeline confirmation), SQ3+SQ4 (architectural template for component selection), SQ6 (alternate AP transport: `VISION_POSITION_ESTIMATE` vs `GPS_INPUT`)


### Source #34
- **Title**: AerialExtreMatch — A Benchmark for Extreme-View Image Matching and Localization (project page + GitHub + Hugging Face dataset)
- **Link**: https://xecades.github.io/AerialExtreMatch/ ; https://github.com/Xecades/AerialExtreMatch ; https://huggingface.co/datasets/Xecades/AerialExtreMatch-Localization
- **Tier**: L1 (peer-reviewed benchmark with public dataset, code, model checkpoints; OpenReview submission)
- **Publication Date**: 2025 (accessed 2026-05-07)
- **Timeliness Status**: Currently valid
- **Target Audience**: Academic + practitioner image-matching evaluators
- **Research Boundary Match**: **Full match** for cross-source UAV-satellite image matching evaluation
- **Summary**: 2025 benchmark with: 1.5 M synthetic train pairs (RGB+depth, diverse UAV/satellite viewpoints); ~30,000 evaluation pairs in 32 difficulty levels stratified by overlap (4 bins: <20/20-40/40-60/>60%), pitch difference (4 bins: 50–55, 55–60, 60–65, 65–70°), and scale (2 bins: 1-2×, >2×); a real-world UAV-localization split captured with DJI M300 RTK + H20T against UAV-derived orthomosaic/DSM AND lower-quality satellite maps. Evaluates 16 representative detector-based + detector-free image matching methods. **This is the academic benchmark our C2+C3 candidate selection must publish numbers against.**
- **Related Sub-question**: SQ1 (academic landscape), SQ7 (datasets)


### Source #35
- **Title**: DARPA Fast Lightweight Autonomy (FLA) program page + Test-and-Evaluation review (arXiv 2504.08122)
- **Link**: https://www.darpa.mil/research/programs/fast-lightweight-autonomy ; https://arxiv.org/abs/2504.08122
- **Tier**: L1 (DARPA program page + 2025 academic review of program results)
- **Publication Date**: program 2015–2018 (concluded); review 2025-04 (accessed 2026-05-07)
- **Timeliness Status**: Foundational reference; review is current (within 18-month authority window)
- **Target Audience**: Defense-program historians + indoor-low-altitude GPS-denied autonomy researchers
- **Research Boundary Match**: **Partial — different regime.** FLA = small quadcopters at ≤20 m/s in cluttered indoor/outdoor with onboard sensing only, no satellite-tile basemap. Our project is fixed-wing, ~17 m/s, 1 km AGL, with sat-tile basemap.
- **Summary**: Foundational US-defense lineage for GPS-denied autonomy (2015–2018, complete). Set the template for "small UAV + onboard sensors + onboard compute → autonomous obstacle-avoidance + navigation without datalink/GPS". Phase 1 in Florida 2017; Phase 2 in Georgia 2018. The 2025 retrospective (arXiv 2504.08122) reviews FLA's testing methodology and Phase 1 results. Companion 2025 USAF SBIR Phase II solicitation (Sweetspot ID `7946c818-409f-5b31-8f06-554466071d83`) is requesting visual-position-and-navigation capability for sUAS in GPS-denied environments — the regulatory tailwind is now active.
- **Related Sub-question**: SQ1 (defense-program lineage)


### Source #36
- **Title**: DSMAC / TERCOM lineage — DTIC ADA315439 (Scene Matching Missile Guidance Technologies) + Wikipedia / SPIE references
- **Link**: https://apps.dtic.mil/sti/tr/pdf/ADA315439.pdf ; https://en.wikipedia.org/wiki/DSMAC ; https://www.spiedigitallibrary.org/conference-proceedings-of-spie/0238/1/Terrain-Contour-Matching-TERCOM-A-Cruise-Missile-Guidance-Aid/10.1117/12.959127.short
- **Tier**: L1 (DTIC unclassified technical report) + L2 (encyclopedia/SPIE proceedings)
- **Publication Date**: DTIC: 1996; SPIE: 1980; Wikipedia: live
- **Timeliness Status**: Foundational baseline (no time window per Step 0.5 — established classical algorithms)
- **Target Audience**: Cruise-missile-class designers; analogues for downward-vision navigation
- **Research Boundary Match**: **Partial — different regime** (cruise missile, terminal guidance). Architectural pattern (pre-cached scene reference + downward camera + correlation matching) is the direct ancestor of our C3 pipeline.
- **Summary**: DSMAC = electro-optical camera correlated against pre-stored reference scenes (often from satellite reconnaissance), achieving 3–10 m terminal accuracy. Tomahawk: TERCOM (radar altimeter + DEM) for mid-flight; DSMAC for terminal. CEP without DSMAC: ~30 m; with DSMAC: "only meters". Gulf War 1991: >80% of 280 launched Tomahawks hit target. **Establishes that downward-vision-against-pre-stored-imagery is a 40+ year-old well-characterised technique class with documented accuracy bounds; our project's claim of <500 m / 99.9% reliability is achievable in the same technique class.**
- **Related Sub-question**: SQ1 (lineage), SQ8 (baseline accuracy expectations)


### Source #37
- **Title**: Electronic Warfare in Ukraine: The Invisible Battle — Ukraine War Analytics
- **Link**: https://ukraine-war-analytics.com/analysis/electronic-warfare-ukraine.html
- **Tier**: L3 (analytical aggregator; primary-source numbers cite vendor / OSINT reports)
- **Publication Date**: live (accessed 2026-05-07)
- **Timeliness Status**: Currently valid (operational-context reference)
- **Target Audience**: Ukraine-deployment practitioners
- **Research Boundary Match**: Full match (operational geography, threat environment)
- **Summary**: Operational-context anchor: Russian EW systems including Pole-21 GPS jammers (25+ km range) plus spoofing capabilities have driven ~70% of small-tactical-UAV losses to EW across the conflict. Twist Robotics' OSCAR cites the same approximate number (~75% of small tactical UAV losses to EW at the front per Source #25). **Confirms the demand-side number is consistent across two independent reporting chains.**
- **Related Sub-question**: SQ1 (Ukraine practitioner perspective)

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## SQ2 — Canonical pipeline decomposition
