<title>Multisensor bias estimation using local tracks without a priori association</title>
Bibliographic record
Abstract
This paper provides a solution for sensor bias estimation based on local tracks at a single time without a priori association for a decentralized multiple sensor tracking system. Each local tracker generates its own local state estimates ignoring the bias. The fusion center then performs track-to-track fusion occasionally after estimating the sensor biases based on the common targets tracked by different sensors. The likelihood function of the bias in a multisensor-multitarget scenario is derived. Using this likelihood, it is shown that the difference of the local estimates is the sufficient statistic for estimating the biases. A least squares solution of the bias estimates and corresponding Cramer-Rao Lower Bound (CRLB) are presented assuming uncorrelatedness as well as accounting for the crosscorrelation between the local estimation errors. Two approaches to estimate the sensor biases in the absence of known track-to-track association, namely, the Maximum Likelihood estimator combined with Probabilistic Data Association (ML-PDA) and an estimator based on soft data association, are proposed. These methods are compared with the baseline solution with known (perfect) track-to-track association by Monte Carlo simulations. The experimental results indicate that the bias estimator based on the soft data association provides nearly optimal performance and has less computational load than the one using ML-PDA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".