Air Target Intention Recognition via Bidirectional Long Short-Term Memory Networks and Hierarchical Maneuver Feature Extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the context of informatized combat, fast and accurate identification of the target’s tactical intentions is a crucial prerequisite for seizing superiority and winning the war. Traditional air target intention recognition methods rely on a large amount of prior knowledge and struggle to effectively capture the characteristic information of time-series data, which fails to meet the objectivity and accuracy requirements of modern battlefield decision-making. Considering that tactical maneuvers are the flight actions taken by target aircraft to achieve tactical intentions, the identification of maneuver types can provide important reference information for predicting tactical intentions. In this paper, an air target tactical intention recognition method combined with maneuver identification is proposed. The motion characteristics of the target are analyzed on the basis of a kinematic knowledge model to identify its maneuver motion. The identified maneuver types, as secondary features of the target’s motion state, are jointly modeled with the selected tactical intention features in a temporal network based on the Bidirectional Long Short-Term Memory (BiLSTM) networks to achieve intention classification. The experimental results demonstrate that the recognition accuracy of the tactical intention inference model combined with maneuver identification can reach 95.76%, which outperforms other recent intention recognition methods. The visualized results using the t-distributed stochastic neighbor embedding technology satisfy certain interpretability requirements. The proposed method effectively improves the recognition capability of air target tactical intention, which is of great significance for efficient battlefield situation analysis and optimized decision-making.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it