Rigid Body Localization and Environment Sensing with 5G Millimeter Wave MIMO
Bibliographic record
Abstract
Accurately localizing a moving target (MT) assisted with 5G in indoor environment could enable a wide variety of new applications. However, an MT in 3-dimensional space is usually considered as a rigid body with six degrees of freedom for industrial applications. Furthermore, the radio-based localization suffers from the non-line-of-sight (NLOS) condition in indoor scenes due to the uncertain environments, which proves to be a main source of location error. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signals, a novel rigid body localization and environment sensing scheme is proposed in this paper. The angle of arrivals (AOAs) derived from 5G channel estimation combined with singular value decomposition (SVD) method is adopted to achieve rigid body position and orientation estimation. Also, we propose a reflection point estimation method by leveraging a hierarchical iterative maximum likelihood-DCS-SOMP (HIML-DCS-SOMP) algorithm to extract the angular information of the single-bounce specular reflections. Simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and sketch the environment information in indoor scene.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".