Pith Number

pith:QSXFQUY3

pith:2026:QSXFQUY3LMI54CDX2Z2RFYNOZF

not attested not anchored not stored refs resolved

Sensing-Assisted LoS/NLoS Identification in Dynamic UAV Positioning Systems

Huijuan Qiao, Jiajing Chen, Lu Bai, Mengyuan Lu, Mingran Sun, Xiang Cheng

A dual-input fusion network identifies LoS/NLoS conditions for UAVs at up to 97.69 percent accuracy by combining RGB images with channel impulse responses.

arxiv:2605.13516 v1 · 2026-05-13 · eess.SP

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\usepackage{pith}
\pithnumber{QSXFQUY3LMI54CDX2Z2RFYNOZF}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Simulation results show that the identification accuracy can reach up to 97.69%, while achieving an improvement of at least 3.59% compared to traditional CIR-only and RGB-only methods. By utilizing the proposed LoS/NLoS identification method, the error of trilateration positioning can be reduced by approximately 70% in a crossroad scenario.

C2weakest assumption

The newly constructed multi-modal dataset accurately represents real-world urban UAV-to-ground propagation conditions across the tested altitudes and scenarios, and the dual-input network successfully bridges the heterogeneous representations of RGB images and CIR data without introducing artifacts that inflate reported accuracy.

C3one line summary

A new dual-input feature fusion network using RGB images and channel impulse responses identifies LoS/NLoS conditions for UAVs with up to 97.69% accuracy and reduces trilateration positioning error by about 70%.

References

30 extracted · 30 resolved · 2 Pith anchors

[1] Low-altitude intelligent transportation: System ar- chitecture, infrastructure, and key technologies, 2024

[2] An efficient UA V localization technique based on particle swarm optimization, 2022

[3] Vision-based UA V self-positioning in low-altitude urban environments, 2023

[4] UA V- assistd wireless localization for search and rescue, 2021

[5] Dynamic positioning of UA Vs to improve network coverage in V ANETs, 2022

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:24.447543Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

84ae58531b5b11de0877d67512e1aec94eb025dfaeae4aeaf87f6ac25cd1e8fa

Aliases

arxiv: 2605.13516 · arxiv_version: 2605.13516v1 · doi: 10.48550/arxiv.2605.13516 · pith_short_12: QSXFQUY3LMI5 · pith_short_16: QSXFQUY3LMI54CDX · pith_short_8: QSXFQUY3

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/QSXFQUY3LMI54CDX2Z2RFYNOZF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 84ae58531b5b11de0877d67512e1aec94eb025dfaeae4aeaf87f6ac25cd1e8fa

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "756607263296734419783b46d4a39af307df566bc14a9016187c283c7b76dea8",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SP",
    "submitted_at": "2026-05-13T13:34:50Z",
    "title_canon_sha256": "6a62eabcb163a87decacd8461db65d55bddd6cf5cff619c40c968263f6d23fdb"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13516",
    "kind": "arxiv",
    "version": 1
  }
}