MétaCan
Menu
Back to cohort
Record W2808427000 · doi:10.1109/radar.2018.8378698

Hybrid beamforming for interference mitigation in MIMO radar

2018· article· en· W2808427000 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 institutionsDefence Research and Development CanadaCarleton University
Fundersnot available
KeywordsBeamformingMIMOJammingCovariance matrixComputer scienceInterference (communication)Electronic engineeringRadarSpace-time adaptive processingAlgorithmAdaptive beamformerTelecommunicationsContinuous-wave radarEngineeringRadar imagingPhysics

Abstract

fetched live from OpenAlex

Hybrid beamforming for multiple input multiple output (MIMO) radar systems in a jamming environment is investigated. A new hybrid beamforming (HB) technique is proposed to reduce the dimensionality of the covariance matrix and to have a better jamming and interference mitigation capability. HB consists of two stages. The first stage decodes, phase shifts the received signals and adds signals decoded by the same code from different antenna elements. The second stage exploits digital beamforming techniques such as Minimum Variance Distortionless Response (MVDR) or convex optimization beamforming to determine the complex weights using N × N covariance matrix where N is the number of transmitting antennas. Simulation results show that the proposed HB technique can achieve better interference and jamming suppression results in comparison with other radar configurations. In addition, the HB technique has the potential to reduce the complexity of MIMO radar signal processing such as space-time adaptive processing.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.591
Threshold uncertainty score0.230

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicRadar Systems and Signal ProcessingFrench-language works237,207