An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target
Bibliographic record
Abstract
Tracking maneuvering targets accurately is one of the most challenging tasks in the design of aircraft tracking systems. For efficient tracking performance, the target motion predicted by the target motion model needs to match the target's actual motion during the maneuver. For tracking a maneuvering target, a combination of the constant velocity (CV) model and the coordinated turn (CT) model with a known turn rate are incorporated in the interacting multiple model (IMM) algorithm. However, in such a scheme when a target performs an unexpected maneuver, the tracking performance deteriorates, or the scheme may even fail to track the target. To overcome this problem, there exists a scheme in the literature, in which instead of using an a priori knowledge of the target turn rate, it is estimated adaptively using the target acceleration and speed. However, this algorithm uses a three-dimensional model to estimate the turn rate in two-dimensional space, which may result in an inaccurate estimation of the target acceleration, and thus may lead to in inaccurate turn rate value. In this paper, an adaptive algorithm to track a maneuvering target in an IMM framework is proposed. Estimating the turn rate is based on the speed of the target and the radius of the turn, where the latter is computed by a simple method using the previous three successive measurements. Further, a detailed study to select an appropriate transition probability matrix for the proposed algorithm is carried out. Simulation results demonstrate that the proposed tracking algorithm outperforms the other algorithms in terms of its tracking accuracy and consistency, particularly in the realistic situation when neither an a priori knowledge about the target turn rate nor about the range rate information is available to the tracking algorithm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".