Evaluation of an MHT-Enabled Tracker with Simulated Multistatic Sonar Data
Bibliographic record
Abstract
Multistatic sonar is an operational concept for jointly deploying and processing multiple sonar sources and receivers in order to obtain enhanced coverage of targets of interest in a volume of ocean. The enhanced performance is obtained through the diversity of "looks" at the targets of interest provided by the many source-target-receiver geometries available through multistatics. This increased probability of target detection by the sensor field is particularly significant in littoral operations, where a tracker must be able to hold the target in the presence of large numbers of random false alarms. This paper describes the performance results of a multiple hypothesis tracking (MHT) enabled tracker against three simulated multistatic data sets provided by the Multi Static Tracking Working Group (MSTWG). Using a set of MSTWG-defined tracking metrics, the tracker is demonstrated to successfully discriminate target contacts from large quantities of false alarms, and in doing so, successfully track the target(s) of interest in each scenario. These results lend credence to the idea that multistatic operations can significantly enhance the ability of a maritime defense force to localize and track targets of interest. Several valuable lessons regarding tracker configuration for multistatic tracking, learned as a result of this research, are presented.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".