Multilateration and Kalman Filtering Techniques for Stealth Intelligence Surveillance and Reconnaissance Using Multistatic Radar
Bibliographic record
Abstract
The research presented in this thesis demonstrated that with a multistatic radar in a 2D plane using the TDOA and FDOA multilateration technique along with the Kalman Filter, to hybrid-geolocate and track a moving stealth target with only two receivers.Hybrid-geolocate and tracking is where the initial location and velocity of the target are unknown.This is an important problem to address because when monitoring boarders between countries, prior knowledge of an incoming target stealth threat is unavailable.Using the Modified Hough Transform, initial and consecutive target locations can be found.Using the dual stage method, reduces the number of receivers required down to two while keeping track of target geolocations.GDOP was used to test boundaries of optimal operation of this radar setup whereas RMSE was used to validate results.This research has shown that in fact hybrid-geolocation and tracking is possible given the dual stage 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".