Passive tracking in heavy clutter with sensor location uncertainty
Bibliographic record
Abstract
In order to address the problem of passive tracking from multiple asynchronous angle-only sensors with location uncertainty in heavy clutter, a new iterative maximum-likelihood probabilistic data-association algorithm is proposed in this paper. An iterative prediction-update framework is adopted in the algorithm to simultaneously estimate the target state as well as the sensor state. At the prediction stage, a deterministic sampling approach is used to adjust the measurement covariance with sensor location uncertainty. Then a two-step grid-search technique is proposed to optimize the log-likelihood ratio, combined with a gradient-based search method. At the update stage, the operational sensor states are updated with target state estimates and measurements in corresponding validation gates. The updated sensor states are used to establish a more accurate log-likelihood ratio in the next iteration, which leads to better parameter estimation. In addition, the effects of the sensor location uncertainty on the track acceptance test and the posterior Craḿer-Rao lower bound are also analyzed. Simulation results show that the proposed method provides a computationally efficient way to improve track initialization performance in heavy clutter with sensor location uncertainty. The proposed work has applications in sonar tracking, geolocation, electronic support measures, and infrared search and tracking systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 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".