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Theoretical Framework for Estimating Target-Object Shape by Using Location-Unknown Mobile Distance Sensors

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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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Hiroshi Saito, Tatsuaki Kimura

Signal Processing · Submitted 16 Feb 2018

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

The authors' main claim To the best of our knowledge, this is the first result in which a target-object shape was estimated by using the data of distance sensors without using their locations.

how it is stated · asserted 0.96 read the record →

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arXiv ID1802.06882 (version 2)
Pith IDINEMPFQ2, Pith's permanent record of this paper and its review
CitationHiroshi Saito, Tatsuaki Kimura. "Theoretical Framework for Estimating Target-Object Shape by Using Location-Unknown Mobile Distance Sensors." arXiv:1802.06882 (2018). https://pith.science/paper/INEMPFQ2
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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.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,837 input tokens.

What the abstract itself states, as probabilities: claims first 0.96 · states limitation 0.62 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.43 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

This paper proposes a theoretical framework for estimating a target-object shape, the location of which is not given, by using mobile distance sensors the locations of which are also unknown. Typically, mobile sensors are mounted on vehicles. Each sensor continuously measures the distance from it to the target object. The proposed framework does not require any positioning function, anchor-location information, or additional mechanisms to obtain side information such as angle of arrival of signal. Under the assumption of a convex polygon target object, each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated. To the best of our knowledge, this is the first result in which a target-object shape was estimated by using the data of distance sensors without using their locations.

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 This paper proposes a theoretical framework for estimating a target-object shape, the location of which is not given, by using mobile distance sensors the locations of which are also unknown. contribution 1.00 0.82 0.26 asserted 0.99
s1 Typically, mobile sensors are mounted on vehicles. background 0.99 0.06 0.02 asserted 0.96
s2 Each sensor continuously measures the distance from it to the target object. method 0.63 0.15 0.02 asserted 1.00
s3 The proposed framework does not require any positioning function, anchor-location information, or additional mechanisms to obtain side information such as angle of arrival of signal. contribution 0.59 0.73 0.10 asserted 1.00
s4 Under the assumption of a convex polygon target object, each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated. method 0.99 0.86 0.37 asserted 1.00
s5 To the best of our knowledge, this is the first result in which a target-object shape was estimated by using the data of distance sensors without using their locations. contribution 0.86 0.86 0.32 asserted 0.96

What kind of paper the classifier reports this is: theoretical result 0.51 · evidence from proof or derivation 0.86.

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

This paper proposes a theoretical framework for estimating a target-object shape, the location of which is not given, by using mobile distance sensors the locations of which are also unknown. Typically, mobile sensors are mounted on vehicles. Each sensor continuously measures the distance from it to the target object. The proposed framework does not require any positioning function, anchor-location information, or additional mechanisms to obtain side information such as angle of arrival of signal. Under the assumption of a convex polygon target object, each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated. To the best of our knowledge, this is the first result in which a target-object shape was estimated by using the data of distance sensors without using their locations.

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How the literature uses it

1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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