Combined Doppler and time free positioning technique for low dynamics receivers
Bibliographic record
Abstract
It has been demonstrated that a position can be estimate in a GPS receiver using only sub millisecond code phase measurements, as opposed to complete pseudoranges. This technique is referred to as time free or snap shot positioning and requires a course estimate of time, an a-priori estimate of the receiver position and GPS satellite ephemeris information. Using assisted-GNSS techniques the GPS satellite information can be obtained from publicly available Internet sources. The approximate time at the receiver can be estimated using a low accuracy clock. This leaves only the a-priori receiver position requirement to an accuracy of approximately half a GNSS code chip (or approximately 150 km for GPS CIA code). Unfortunately, there is no universally available solution to this last requirement as the receiver could be anywhere on or near the Earth. This last restriction has the potential to limit the usefulness of the time free positioning technique. We have developed a combined Doppler assisted time free navigation methods which requires no knowledge of the a-priori receiver position. We propose that first the position can be estimated with no a-priory knowledge of the receiver position using only Doppler measurements. The accuracy of this estimate is not useful for navigation, but it is within the initialization requirements of the time free positioning method. This Doppler estimate will provide a reasonable initialization to the time free algorithm and thus effectively removes the requirement for a-priori position knowledge when estimating a receiver position using short snap shots of data, as is often the case in software receivers. This combined technique eliminates a cumbersome requirement and improves the effectiveness of receivers being designed using short bursts of data and software radio processing techniques. This paper will give an overview of the combine Doppler, time free positioning method and presents a discussion on the performance of both methods under various initial conditions. The method has been developed and tested with a software receiver and tested with off-air signals. Additionally, simulations will be presented that asses the limitations of this technique with regard to receiver dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".