Passive Coherent Location With Unknown Transmitter States
Bibliographic record
Abstract
This paper addresses the problem of passive coherent location (PCL) using a mobile transmitter with unknown state as an illuminator of opportunity. First, the necessary and sufficient conditions for local observability are derived using the Fisher information matrix (FIM). Second, a two-point track initialization approach, together with an efficient numerical search method, is proposed to initialize the target state as well as the transmitter state. Third, a recursive multitarget tracking algorithm with the proposed PCL system, in which the number of targets is unknown in the presence of measurement origin uncertainty, false alarms, and missed detections, is presented. The target state and the transmitter state are estimated in a sequential manner. In order to improve track continuity, a two-level gating technique is adopted in the data association phase of the proposed algorithm. Fourth, the posterior Cramer-Rao lower bound (PCRLB), with measurement origin uncertainty and transmitter state uncertainty, is derived to provide the optimal theoretical performance bound. Simulation results show the effectiveness of the proposed 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".