Multiple Detection Probabilistic Data Association filter for multistatic target tracking
Bibliographic record
Abstract
Abstract—A standard assumption in most tracking algorithms, like the Probabilistic Data Association (PDA) filter, Multiple Hy-pothesis Tracker (MHT) or the Multiframe Assignment Tracker (MFA), is that a target is detected at most once in a frame of data used for association. This one-to-one assumption is essential for correct measurement-to-track associations. When this assumption is violated, the above algorithms treat the extra detections as random clutter. When multiple detections from the same target fall within the association gate, the PDA filter tries to apportion the association probabilities, but with the fundamental assumption only one of them is correct. The MFA and the MHT algorithms try to spawn multiple tracks to handle the additional measurements from the same target, assuming at most one measurement came from each target. Both of these approaches have undesirable side effects since they ignore the possibility of multiple detections from the same target in a scan of data. Such multiple detection situations occur in multistatic tracking problems. In this paper, we proposed a new Multiple Detection Proba-bilistic Data Association (MD-PDA) filter for tracking a target when more than one target originated measurement may exist within the validation gate. In the proposed MD-PDA, combina-torial association events are formed to handle the possibility of multiple measurements from the same target. Modified associa-tion probabilities are calculated with the explicit assumption of multiple detections. Simulations are presented to demonstrate the effectiveness of the algorithm on a single target tracking problem in clutter. Extensions to handle multiple targets using the Joint PDA, MHT and MFA approaches are under development.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
New probabilistic data association filter for target tracking; a signal-processing algorithm.
This paper develops a target-tracking algorithm and does not study research practice.
Signal-processing filter for multistatic target tracking; engineering domain research.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".