Drifting Error Propagation Analysis in Displacement Increments Estimation Using INS, TDCP, and Doppler-Based Algorithms for Dynamic Positioning
Bibliographic record
Abstract
Accurate displacement increments estimation is crucial for reliable navigation in various applications, including autonomous systems and geospatial positioning. This study compares three different displacement increments estimation algorithms: Inertial Navigation System (INS), Time-Differenced Carrier Phase (TDCP), and Doppler-based algorithms. INS is a classical dead reckoning algorithm, which is independent of external conditions and estimates displacement increments by integrating acceleration measurements. TDCP and Doppler-based positioning algorithms using global navigation satellite systems (GNSS) can also provide high-accuracy displacement increments by getting the between-epoch average velocity and the instantaneous velocity, respectively. Since TDCP is susceptible to carrier phase cycle slip thereby a cycle-slip detection algorithm is required. Doppler-based algorithm estimates displacement increments using Doppler frequency shift. As displacement increments estimation algorithms, the positioning errors with those algorithms all suffer from drift over time. However, there is no research comparing the performance of these three algorithms in terms of error drifting. In this paper, the performance of these algorithms is evaluated with respect to accuracy and drift characteristics using a vehicle-based driving dataset of INS measurements and GNSS observations. The results show that INS suffers from significant drift over time. Whereas the Doppler-based algorithm offers stable displacement increments estimation but has a lower accuracy than TDCP. Finally, TDCP provides more accurate displacement increments estimation results than Doppler and INS when the cycle slip is properly handled. The RMS of TDCP positioning errors are 0.042 m and 0.169 m for 60 s period in horizontal and vertical directions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".