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Record W4322753796 · doi:10.21203/rs.3.rs-2625303/v1

Digital Pulse Compression: Linear Frequency Modulation Approach

2023· preprint· en· W4322753796 on OpenAlex
Mallikarjuna Gowda, G K Siddesh, H Anu, S. Chaithanya, Rana Gill, M. Ijaz Khan, Sayed M. Eldin

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

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsPulse compressionPulse repetition frequencyComputer scienceRadarElectronic engineeringSIGNAL (programming language)Pulse (music)Power (physics)TransceiverModulation (music)Matched filterPulse-width modulationFilter (signal processing)AcousticsTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In radar system a challenging task is to achieve a good range resolution with a shorter pulse which needs more peak power. High peak power makes the design of transceivers complex because the devices used to build transceivers are able to withstand with high peak power. In literature pulse compression method is widely used to minimize the problem of using high peak power signal. In this paper, the problem of generating a high peak power signal with shorter duration, which is used to detect only moving targets has been addressed by proposing a novel radar pulse compression method using Linear Frequency Modulation with improved range resolution with a basic construction of a pulse processor which is analogous to a standard matched filter, and is reformed to suit an application. Multiple code types and lengths needed are used due to the flexibility in the design which reduce an uncertainties affected by the use of high pulse repetition frequencies.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.107
GPT teacher head0.355
Teacher spread0.248 · 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