Multiple Model Multi-Bernoulli Filters for Manoeuvering Targets
Bibliographic record
Abstract
The cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter is a recursive, multitarget tracking mechanism based on the random finite set (RFS) theory using the finite set statistics (FISST) framework. It provides an estimate of the number of targets in a given scenario space, along with the most likely locations of those targets. It also provides this estimate without the expensive operation of multidimensional assignment between measurements and target estimates. Unlike other RFS methods, the CBMeMBer filter outputs an estimate of the actual multitarget probability density function. Current implementations include a nonlinear sequential Monte Carlo (SMC) approximation, as well as an analytical Gaussian mixture (GM) solution. A new MeMBer recursion for tracking multiple targets traveling under multiple motion models is introduced. The multiple model CBMeMBer (MM-CBMeMBer) filter presented here uses jump Markov models (JMM) to extend the standard CBMeMBer recursion to allow for multiple target motion models. This extension is implemented using both the SMC- and GM-based CBMeMBer approximations. The recursive prediction and update equations are presented for both implementations. Each multiple model implementation is validated against its respective standard CBMeMBer implementation, as well as against each other. This validation is done using a simulated scenario containing multiple manoeuvering targets. A variety of metrics, including estimate accuracy, model detection capability, and algorithm computational efficiency are used for performance evaluation. The new method is shown to improve results in several metrics with only a minor increase in computational complexity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".