Pith Number

pith:BPOQPAHV

pith:2026:BPOQPAHVP27X2MNEH242YKXIK6

not attested not anchored not stored refs resolved

PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams

Andreas Porada, Christopher Mutschler, Felix Ott, Peter Bauer, Tobias Feigl

A modular neural network breaks pedestrian dead reckoning into separate estimators for orientation and velocity that are fused with uncertainty measures to track fast movements without accumulating errors.

arxiv:2605.15252 v1 · 2026-05-14 · cs.LG · cs.AI · eess.SP

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{BPOQPAHVP27X2MNEH242YKXIK6}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Experiments on dynamic sports movement data show that PDRNN achieves superior accuracy and precision compared to classic and ML-based methods, effectively avoiding error accumulation common in black-box approaches.

C2weakest assumption

The assumption that separate ML-based models can reliably estimate orientation, undirected velocity or distance from acceleration and gyroscope data while a final fusion model can combine these outputs using uncertainty estimates to handle discrepancies in sampling rates and dynamic movements.

C3one line summary

PDRNN is a modular hybrid RNN-based system for fusing loosely coupled inertial and radio signals to achieve more accurate and robust pedestrian dead reckoning during high-acceleration movements.

References

52 extracted · 52 resolved · 0 Pith anchors

[1] RIDI: Robust IMU double integra- tion, 2018

[2] Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, and new methods, 2019

[3] DL-RNN: An accurate indoor localization method via double RNNs, 2019

[4] Smartphone-based traveled distance estimation using individual walking patterns for indoor localization, 2018

[5] Stride length and speed for adults, children, and fossil hominids, 1984

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:48.597586Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0bdd0780f57ebf7d31a43eb9ac2ae8579901507d0acbd2186d0d921638282f58

Aliases

arxiv: 2605.15252 · arxiv_version: 2605.15252v1 · doi: 10.48550/arxiv.2605.15252 · pith_short_12: BPOQPAHVP27X · pith_short_16: BPOQPAHVP27X2MNE · pith_short_8: BPOQPAHV

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/BPOQPAHVP27X2MNEH242YKXIK6 \
  | 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: 0bdd0780f57ebf7d31a43eb9ac2ae8579901507d0acbd2186d0d921638282f58

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ff5e023fbf4d3c4bbed60e22649fb75633d2e9bde9a70b9e3bbeb94c59fd544d",
    "cross_cats_sorted": [
      "cs.AI",
      "eess.SP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T16:35:57Z",
    "title_canon_sha256": "b566881a94f89fa11d2440df6b7b47487be55a14efad71928dd1a156693c4585"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15252",
    "kind": "arxiv",
    "version": 1
  }
}