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Linear FMCW Radar System for Accurate Indoor Localization and Trajectory Detection

2020· article· en· W3014081040 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2020 International Conference on Computing, Networking and Communications (ICNC) · 2020
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsConstant false alarm rateComputer scienceContinuous-wave radarRadarKalman filterDetectorTrajectoryPosition (finance)Doppler effectDoppler frequencyComputer visionDoppler radarPulse-Doppler radarRadar imagingArtificial intelligenceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The frequency modulated continuous wave (FMCW) radar has been used to determine the position and movements of a target. In this paper, we design a liner FMCW radar-based indoor position system for better target localization and trajectory detection. First, with the received radar signals, we construct a two-dimensional range-Doppler map. Using the map, We extend the one-dimensional constant-false-alarm rate (CFAR) technique into the two-dimensional one and develop a two-dimensional cell-averaging CFAR detector; it effectively eliminates the outliers of the original CFAR results and hence reduces localization error significantly. Finally, we adopt the Kalman filter to optimize the target trajectory. The experimental results are provided and they show that the accuracy of the estimated range is improved by about 38% and 11% in stationary and moving target localization, respectively, as compared with the current detectors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.268
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it