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Record W7100119688

THE UNIVERSITY OF CALGARY Selected GPS Receiver Enhancements for Weak Signal Acquisition and Tracking

2007· article· en· W7100119688 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemRangingBandwidth (computing)False alarmBasebandBinary offset carrier modulationKalman filterSensitivity (control systems)Signal processing
DOInot available

Abstract

fetched live from OpenAlex

ii The sensitivity of a baseband signal processing unit, including both acquisition and tracking, is critical for a GPS receiver to function in perturbed signal enviroments such as indoors and under ionospheric scintillation.. To improve acquisition sensitivity, the dif-ferential combining approach, which allows a 2.5 dB improvement in processing loss as compared to noncoherent methods, is proposed herein. This results in a sensitivity im-provement ranging from 1.2 dB to 1.6 dB for a given probability of false alarm and mi-ssed detection. Based on an analysis of the Costas-family PLLs, carrier tracking is enhanced by optimiz-ing loop configurations. The decision-directed loop, exhibits a 2-dB sensitivity im-provement as compared to other types of PLLs. Bandwidth and root deviation caused by bilinear or boxcar transforms are solved by implementing a root-controled method. Using this design makes the bandwidth of the digital loop close to desired values, and thus im-proves carrier tracking by another 1 to 2 dB. A Kalman Filter adopts a soft-mode to deal with the bit sign uncertainty and adjusts the bandwidth to minimize the mean square car-rier tracking error. This leads to a 4 dB and 7 dB sensitivity improvement during weak and strong amplitude ionospheric scintillations, respectively. iii

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.130

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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 designBench or experimental
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

Citations0
Published2007
Admission routes1
Has abstractyes

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