Multisensor joint tracking and identification using particle filter and Dempster-Shafer fusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simultaneous multi-target tracking and identification using multiple radar sensors is advantageous to offer more reliable real-time information for situation assessment, resource management and decision making, which is essentially a problem of joint tracking, association, identification and sensor fusion. This paper first presents a method to use the Rao-Blackwellised particle filter (RBPF) based approach to address the joint multitarget tracking, association and identification in presence of clutter using a single radar kinematic measurement. Using the particle filter as an association indicator, the data association is efficiently integrated into the RBPF frameworks. To achieve more robust and reliable performance, multi-sensor fusion is exploited. Dempster-Shafter (D-S) belief function is then incorporated into the RBPF framework under the transferable belief model (TBM) to provide a flexible fusion result. Computer simulations using the proposed schemes show reliable tracking and reasonable and correct target classification with great flexibility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it