Radar Data Clustering and Bounding Box Estimation with Doppler Measurements
Bibliographic record
Abstract
High-resolution automotive radars, which are widely used nowadays, yield multiple measurements per frame from a single target. Clustering these measurements accurately and finding the tight bounding boxes are two challenging problems. In this work, the shape is estimated using a rectangular bounding box using the position and range rate measurements from the radar. While the Doppler (or range rate) measurements provide extra information about the target velocity, the presence of micro-Doppler (for example, returns from tires of a car) can significantly degrade the clustering, bounding box and heading estimates. It is necessary to cluster the measurements corresponding to different targets, as well as those that occur due to micro-Doppler. A clustering method is developed that can effectively use the Doppler information to differentiate closely spaced targets while avoiding the drawbacks of microDoppler. The bounding box estimate is refined by using only the measurements corresponding to the target bulk and, in turn, further aids in clustering iteratively. The effectiveness of the proposed approach is verified using simulations for different scenarios.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".