MétaCan
Menu
Back to cohort
Record W4394983015 · doi:10.23977/jeis.2024.090118

Research on Anti-Jamming for Vehicle Millimeter Wave Radar Based on Frequency Hopping Technology

2024· article· en· W4394983015 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electronics and Information Science · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRadarFrequency-hopping spread spectrumExtremely high frequencyJammingRadar jamming and deceptionComputer scienceElectronic engineeringAcousticsTelecommunicationsPhysicsEngineeringPulse-Doppler radarRadar imaging

Abstract

fetched live from OpenAlex

To address the interference issues among vehicle millimeter wave radars, this chapter proposes an anti-jamming technique that involves frequency hopping of the signal at the transmitter side. By varying the frequency of the radar transmission signal and based on different frequency hopping sequences, it is possible to distinguish between the transmission signal and interference signals, thereby achieving the purpose of interference suppression. This study validates the anti-jamming performance of the proposed solution under different interference conditions through theoretical analysis and simulation. The simulation results indicate that the scheme can effectively suppress interference in scenarios with both single and multiple targets, enhancing the system's stability and reliability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.335
Teacher spread0.307 · 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