Performance comparison of Kalman filter and maximum likelihood carrier phase tracking for weak GNSS signals
Bibliographic record
Abstract
GNSS carrier phase measurement capability is one of the most important features for high performance/accuracy receivers. However significant signal attenuation due to blockage and multipath as experienced indoors for example, degrades carrier phase quality in standard carrier phase tracking loops using PLL or KF-PLL. Under such conditions, the receiver may not be able to generate reliable carrier phase measurements either due to weak signal, fading or fragility of conventional tracking loops. One contribution of this paper is the proposed decoupled maximum likelihood carrier phase, Doppler and Doppler rate tracking architecture. This approach first independently estimates Doppler rate and Doppler based on power spectra and then estimates the carrier phase if possible. In order to assess the actual carrier phase measurement quality, controlled simulation is also conducted after analyzing the overall phase tracking performance. Double differenced carrier phase results as well as RTK solutions show that the proposed method works well and is able to generate reliable carrier phase measurement and centimeter level solution when the carrier-to-noise-density ratio (C/N0) is about 20 dB-Hz.
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 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".