Single target tracking in clutter: performance comparison between pda and vda
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
The dynamic programming algorithm, called the Viterbi algorithm PA), is an algorithm for finding the best path through the nodes of a trellis by minimizing the summed cost. It is widely used in estimation and detection problems in digital communications and signal processing. It hos been also used in the speech and character recognition where the speech signals or characters are modeled by hidden Markov models. Recently, the Viterbi algorithm has been employed in data association and forget tracking, namely; the Viterbi Data Association FDA) algorithm. The target motion is assumed to be a Markov process so that the cost increment over two consecutive time instances is only dependent on the states in these instances. In this paper, two dyerent PDA algorithms have been analyzed and compared to the standard Probabilistic Data Association (PDA) filter. Also, the perjomance of the algorithms is compared through simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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