Joint Localization and Environment Sensing of Rigid Body With 5G Millimeter Wave MIMO
Bibliographic record
Abstract
Accurately localizing a target in three-dimensional (3D) space assisted with the fifth generation (5G) wireless systems in an indoor environment could enable a wide variety of new applications, including precise control for factory automation, self-maneuver of the vehicle and so on. However, controlling a target in 3D space relies on a rigid body modelling with six degrees of freedom, which dramatically increases the localization difficulty and complexity. Furthermore, for radio-based localization methods, the lack of line-of-sight (LOS) and the existence of reflection points in the environment will also influence the rigid body localization process. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signal, a novel rigid body joint active localization and environment sensing scheme is proposed in this paper. Specifically, the multi-path effect of millimeter wave (mm-wave) signal with a single reflection can be exploited to enhance the rigid body localization accuracy, and it can also be utilized to locate the reflection points, which further enables a new way for environmental sensing. Hence, we first propose a two-step hierarchical compressive sensing algorithm to extract the angular and distance information of the LOS (if available) and single-bounce specular reflections. Then a particle swarm optimization (PSO) based method is derived to recover the posture of the rigid body and the location of reflection points. The Cramér-Rao lower bound (CRLB) on angle, rigid body posture and reflection points position uncertainty is also analyzed. The simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and locate the reflection points around the rigid body even under obstructed-line-of-sight (OLOS) conditions in an indoor scene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".