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Joint Detection and Tracking for Compact HFSWR

2023· article· en· W4386630867 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsTracking (education)Computer scienceDetectorArtificial intelligenceComputer visionRadar trackerLow probability of intercept radarTracking systemTrack-before-detectTransmission (telecommunications)Object detectionRadarRadar imagingPattern recognition (psychology)TelecommunicationsRadar engineering detailsKalman filter

Abstract

fetched live from OpenAlex

Due to reduced aperture size of the receiving antenna array and low transmit power, compact high-frequency surface wave radar suffers from low positioning accuracy and low detection probability, which raises challenges to target detection and tracking. Under such circumstances, traditional detection before tracking methods in which one-way information transmission from the detector to the tracker is used often lead to false tracking and track fragmentation due to missed detections and interferences. To address this problem, a joint detection and tracking processing framework which realizes a two-way target information transmission between the detector and tracker is proposed in this paper. Experimental results using field data show that the average tracking time on target obtained by the proposed method is 17.5 minutes longer than that of the detection before tracking method. The plot-to-track association accuracy and target tracking continuity is significantly improved.

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: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.147

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 head0.038
GPT teacher head0.238
Teacher spread0.200 · 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

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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