Measurement and Track Fusion with Disparate Sensors
Bibliographic record
Abstract
It is an assumed fact that measurement fusion will always produce better results than track fusion at the system level. Decisions have been made given this assumed fact, and many trade-offs have been made due to communication limitations. For some systems, only the source tracks are communicated to the system level due to these constraints. It also depends to some degree on the expected characteristics of the flight path of the object that is being tracked. Therefore, the decision of whether to fuse measurements or source tracks at the system level is made given the communication limitations and the nature of the objects that are being tracked. This paper extends our previous work on this subject. For the first phase of this work, a three-sensor scenario was created with all measurements being sent to the system level where a composite tracker filtered in all of the measurements. In parallel for comparison, the source tracks were sent to the system level where a network tracker fused the source tracks together. The second phase of this work modified the scenario to increase the measurement rate of the local sensor, but only communicated the measurements to the system level at the same rate that the source tracks were transmitted. The rate of the local measurements was at 10 hertz, but measurements and source tracks were sent only at a one hertz rate. The results from this setup produced some interesting take-aways. Now in phase three, the scenario is enhanced to change to make the three sensors in the scenario disparate in their resolution capabilities. The setup, results, and conclusions for this updated scenario are presented in this paper. The overall goal of this effort is to explore the situations when measurement fusion is warranted given the extra "expense" of communications.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".