Enhancing Doppler Ego-Motion Estimation: A Temporally Weighted Approach to RANSAC
Bibliographic record
Abstract
Ego-motion estimation from millimetre-wave Doppler are subjected to high levels of outliers caused by multipath reflections. A common method to reduce the effect of outliers is to fit measurement points to a velocity model using the random sample consensus (RANSAC) algorithm, where the velocity model is traditionally parameterized from data collected from a single time step. In this paper, we show that the temporal relationship of data points between successive measurements can be exploited to improve velocity estimation. Two variations of RANSAC with an integrated weighted sliding window are proposed. Each point in the window receives a weight that decreases over time. In the first algorithm, points are sampled with a temporally weighted probability and the velocity model is generated using least square regression (LSQ). In the second algorithm, points are sampled with a uniform probability but the model is parameterized with a temporally weighted LSQ. The motion of the platform is then calculated for both methods. Experimental results using data from three indoor locations demonstrate an average position accuracy improvement of 19.5% over conventional RANSAC implementations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".