High accuracy positioning with low-cost single-frequency GPS system in multipath environments
Bibliographic record
Abstract
Positioning using low-cost, single-frequency GPS receivers provides an economical solution, but these receivers are subject to biases leading to degradation of the accuracy required. Factors contributing to degradation in the accuracy of low-cost systems are ionospheric delay, multipath, and measurement noise. Unless carefully addressed, these errors distort the ambiguity resolution process, and result in less accurate positioning solutions. However, with the modern hardware improvements, measurement noise is now almost neglibible. Ionospheric delay has been dramatically reduced with the availablity of global or local ionospheric maps produced by various organizations (e.g., International GNSS Service (IGS), and National Oceanic and Atmospheric Administraion (NOAA). The major remaining constraint and challenging problem is multipath. This is because mulitpath is environmentally dependant, difficult to model mathematically, and cannot be reduced through differential positioning. The research proposes a new approach to identify multipath-contaminated L1 measurements. The approach is based on wavelet analysis using Daubechies family wavelets. First, the difference between the code and carrier phase measurements was 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 existed, and high-frequency part of the residual component described above. The L1 measurements obtaines from the staellites with lowest multipath were used to compute the final positions using Trimble Total Control (TTC) and Bernese scientific processing software packages. The AC12 single-frequency GPS receiver was extensively tested in static and kinematic modes. Accuracies within 5 cm was demostrated 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 | 0.001 |
| 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".