Precise GPS Positioning with Low-Cost Single-Frequency System in Multipath Environment
Bibliographic record
Abstract
Low-cost, single-frequency GPS systems provide economical positioning solutions to many geomatics applications, including GIS and low-accuracy surveying applications. Unfortunately however, the positioning accuracy obtained with those systems is not sufficient for many surveying applications. This is mainly due to the presence of ionospheric delay and multipath. In this research ionospheric delay is accounted for using regional high-resolution ionospheric maps produced by the US National Oceanic and Atmospheric Administration (NOAA). The major remaining constraint and challenging problem is multipath. This is because multipath is environmentally-dependent, difficult to model mathematically and cannot be reduced through differential positioning. This research proposes a new approach to identify multipath-contaminated L1 measurements through wavelet analysis. First, the difference between the code and carrier-phase measurements is estimated, leaving essentially twice the ionospheric delay, multipath and system noise. The ionospheric delay is largely removed by using high-resolution ionospheric delay maps produced by NOAA. The remaining residuals contain mainly low-frequency multipath, if it exists, and high-frequency system noise, which are decomposed using Daubechies family wavelets (db8). A satellite signal is identified as contaminated by multipath based on the standard deviation of the low-frequency part of the residual component. The L1 measurements obtained from the satellites with the lowest multipath are used to compute the final positions using two software packages, namely Trimble Total Control (TTC) and Bernese scientific processing software. The Magellan AC12 low-cost single-frequency GPS receiver was extensively tested in static mode. It is shown that accuracies within 5 cm are routinely obtained for baselines up to 65 km under various multipath environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".