PHD filtering for tracking an unknown number of sources using an array of sensors
Bibliographic record
Abstract
Direction of arrival (DOA) tracking of an unknown number of sources in a highly non-stationary environment is considered. Conventional DOA estimation techniques, such as MUSIC, fail when the stationary assumption is violated. Furthermore, the time-varying number of sources makes the problem even more challenging. Recently, a particle filtering approach, which propagates the approximate posterior of the target states and then adopts a reversible jump Markov chain Monte Carlo (RJMCMC) diversity step to resolve the number of targets, was proposed. However, this algorithm is sensitive to incorrect model order initialization. In this paper, we propose a new algorithm for tracking an unknown number of sources based on the probability hypothesis density (PHD) filter, which propagates only the first moment of the joint posterior distribution of targets in terms of particles, as a computationally efficient alternative to the RJMCMC method. The PHD algorithm provides an automatic way to estimate the number of sources, eliminating the need for a separate model order initialization or update step, which is typically the source of problem in particle-filtering based methods. In addition to the fact that the PHD implementation is simple, simulation results show that, the PHD implementation yields superior performance over the other method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".