MétaCan
Menu
Back to cohort
Record W2209818189 · doi:10.1109/upinlbs.2014.7033705

Weak GPS signal acquisition using antenna diversity

2014· article· en· W2209818189 on OpenAlexaff
Mohammad Mozaffari, Ali Broumandan, Kyle O’Keefe, Gérard Lachapelle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultipath propagationGlobal Positioning SystemGPS signalsFalse alarmComputer scienceFadingAssisted GPSDoppler effectReal-time computingSIGNAL (programming language)Electronic engineeringTelecommunicationsEngineeringArtificial intelligenceDecoding methodsPhysics

Abstract

fetched live from OpenAlex

Signal acquisition is the first operation stage of a GPS receiver that detects the presence of GPS signals and provides a coarse estimate of the code delay and Doppler frequency. GPS signal acquisition becomes a challenging problem when the signal is subject to attenuation and multipath fading. In practice, standard methods such as extending the integration time without any aiding information to increase processing gain are not always sufficient to acquire weak signals. This difficulty can be characterized by the probability of detection, false alarm and mean acquisition time. This paper exploits spatial antenna diversity to mitigate the multipath fading effect in the acquisition process. Equal gain combining of two independent antennas is considered in a standalone processing strategy. The performance is evaluated in terms of Receiver Operating Characteristic (ROC). The theoretical analysis is compared with Monte Carlo simulations and real GPS data results. Experimental results demonstrate the improvement of detection probability and enhanced immunity against false alarms in dense multipath environments.

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 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 score0.896
Threshold uncertainty score0.341

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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicGNSS positioning and interferenceFrench-language works237,207