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A Combined Dynamic Mode Decomposition–Constant False Alarm Rate Algorithm for Near-Real-Time Moving Target Detection

2025· article· W7124989675 on OpenAlexaff
Andrej Vukovic, Thomas Forsdyke, Shashank Pant, Pascale Sévigny, Bhashyam Balaji, Sreeraman Rajan

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsDefence Research and Development CanadaNational Research Council CanadaCarleton University
Fundersnot available
KeywordsConstant false alarm rateRadarFalse alarmTrack-before-detectMode (computer interface)Radar trackerMoving target indicationHilbert–Huang transformRadar detection

Abstract

fetched live from OpenAlex

We present a novel form of constant false alarm rate (CFAR) target detection based on dynamic mode decomposition (DMD). Our algorithm runs in near-real-time and can be applied in any setting in which time series data is used for target detection, including radar and sonar. We test our algorithm using a simulated radar target injected into real-world V-band frequency modulated continuous wave (FMCW) radar data. We demonstrate that combining DMD with CFAR results in improved probabilities of false alarm and detection.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.282
Teacher spread0.278 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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