A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems
Bibliographic record
Abstract
A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".