Observability Analysis of Multipath Assisted Target Tracking with Unknown Reflection Surface
Bibliographic record
Abstract
This paper analyzes the problem of incorporating multipath measurements from an unknown reflection surface for improving the tracking result of a single target. The characteristic of the reflection surface, such as location and the slope, should be known in order to use the multipath measurements for track initialization or filtering. However, in a real-world problem, the reflection surface is mostly unknown or partial information about the reflection surface is known. If the problem of estimating the unknown parameters of the reflection surface is observable with the direct and multipath measurements, then the multipath measurements from the unknown reflection surface could be used to improve tracking performance. In this paper, a tracking framework is proposed to track a single target with an unknown reflection surface, and the Fisher Information Matrix (FIM) is derived for the considered problem to examine the observability. In addition, simulation results showing the performance of multipath-assisted tracking and the performance bounds are also provided.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".