Comprehensive Time-Offset Estimation for Multisensor Target Tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temporal registration of sensors is an essential preprocessing step in multisensor target tracking systems. A new approach for multisensor time-offset estimation is proposed in this article. First, the time offset pseudomeasurement equation is derived and calculated in both centralized and decentralized scenarios, where measurements and local tracks are available at the fusion center, respectively. The observability of time offset is analyzed theoretically, which shows that only relative time offsets between sensors are observable. Second, a two-sensor two-stage filtering method is developed with four different formulations corresponding to different time-offset statistical models and target dynamic models to obtain a relative time-offset estimate. A multisensor two-stage filter is also proposed to obtain a minimum-bias time-offset estimate. Furthermore, the interacting multiple model estimator is used to deal with temporal registration in the presence of target maneuvers. Finally, the posterior Cramér-Rao lower bound (PCRLB) is derived for relative time-offset estimation. Simulation results show that the proposed algorithm with two sensors yields an empirically unbiased estimate of the relative time offset, and that the root-mean-square errors (RMSEs) match the corresponding PCRLB. Simulation results for multisensor target tracking are also presented to demonstrate the validity of the proposed algorithms.
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.
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 | 0.000 |
| 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 it