Integrated bias removal in passive radar systems
Bibliographic record
Abstract
A passive coherent location (PCL) system exploits the ambient FM radio or television signals from powerful local transmitters, which makes it ideal for covert tracking. In a passive radar system, also known as PCL system, a variety of measurements can be used to estimate target states such as direction of arrival (DOA), time difference of arrival (TDOA) or Doppler shift. Noise and the precision of DOA estimation are main issues in a PCL system and methods such as conventional beam forming (CBF) algorithm, algebraic constant modulus algorithm (ACMA) are widely analyzed in literature to address them. In practical systems, although it is necessary to reduce the directional ambiguities, the placement of receivers closed to each other results in larger bias in the estimation of DOA of signals, especially when the targets move off bore-sight. This phenomenon leads to degradation in the performance of the tracking algorithm. In this paper, we present a method for removing the bias in DOA to alleviate the aforementioned problem. The simulation results are presented to show the effectiveness of the proposed algorithm with an example of tracking airborne targets.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".