Indoor Point Cloud Imaging With Millimeter-Wave Radar Based on Target Segmentation
Bibliographic record
Abstract
To address the difficulty of accurately distinguishing static and moving targets with a single millimeter-wave radar in multitarget scenarios—which impacts self-velocity estimation accuracy—this article proposes a target segmentation-based millimeter-wave radar indoor point cloud imaging method (TSMIP). On our collected mmWave radar dataset, the proposed method achieves 83.89% segmentation accuracy, 39.42% higher than MobileNetV3 with 10 times fewer parameters. Compared to ResNet50, it is only 0.43% less accurate while reducing parameters by 100 times. Against the latest lightweight network, it cuts parameters by 46.44% with just a 0.23% drop in accuracy. The runtime of lightweight target segmentation network is reduced by 57% and 42% compared to ResNet50 and MobileNetV3, respectively. In addition, imaging results show that TSMIP maintains robust performance in environments with multiple moving targets. TSMIP is unaffected by the speed of moving pedestrians, ensuring stable, and accurate point cloud data. It avoids issues like scattering, which can degrade image quality. This technology is suitable for unmanned devices in smart industrial environments, where precise radar-based imaging is crucial.
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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 | 0.001 |
| 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".