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Localization with Noisy Android Raw GNSS Measurements

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Xu Weng, Keck Voon Ling

Signal Processing · Submitted 16 Sep 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method.

how it is stated · asserted 1.00 read the record →

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arXiv ID2309.08936 (version 2)
Pith IDN2SQUX5L, Pith's permanent record of this paper and its review
CitationKeck Voon Ling, Xu Weng. "Localization with Noisy Android Raw GNSS Measurements." arXiv:2309.08936 (2023). https://pith.science/paper/N2SQUX5L
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,323 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.41 · human participants 0.05 · controlled comparison 0.93 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.96 · mentions code or data availability 0.99.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. background 1.00 0.07 0.03 hedged possibility 0.81
s1 The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. background 0.90 0.13 0.02 asserted 1.00
s2 This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. method 0.62 0.78 0.06 asserted 0.99
s3 Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. result 1.00 0.96 0.87 asserted 1.00
s4 Our codes are available at https://github.com/ailocar/androidGnss. contribution 0.87 0.49 0.02 asserted 0.94

What kind of paper the classifier reports this is: method or tool 0.58 · evidence from observation or experiment 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss.

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