A parallel filtering technique for a surveillance radar
Bibliographic record
Abstract
Tracking algorithms commonly use some practical models of target motion to estimate the present and the future target kinematics quantities such as the position, the velocity and in certain cases, the acceleration. When there is a maneuver, the tracking algorithm will detect the error created by the change in target motion and correct the situation to adapt itself to this new change or new tracking model. There are different approaches in the literature for handling maneuver detection such as parallel filtering techniques. These techniques are used mainly because of quick response in maneuver detection of moving objects and to enhance the position and the speed estimations with filtering stability. The purpose of the study is to show the benefit of using a parallel filtering technique over a single filter. The paper presents a parallel filter design using three extended Kalman filters (EKF) with a simple switching algorithm for maneuver detection. The state vector of the EKF is in cartesian coordinates and the measurement vector is in polar coordinates. This design is relatively simple compared to other parallel Kalman filter techniques and requires modest computer resources. The simulation results have shown improvement using parallel filtering, particularly in smoothing speed estimations and the rapidity of convergence to track the target after it has abruptly maneuvered.>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".